Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their low data rates severely limit broader applications. We present AquaScope, the first acoustic communication system capable of underwater image transmission on commodity mobile devices. To address the key challenges of underwater environments -- limited bandwidth and high transmission errors -- AquaScope employs and enhances generative image compression to improve compression efficiency, and integrates it with reliability-enhancement techniques at the physical layer to strengthen error resilience. We implemented AquaScope on the Android platform and demonstrated its feasibility for underwater image transmission. Experimental results show that AquaScope enables reliable, low-latency image transmission while preserving perceptual image quality, across various bandwidth-constrained and error-prone underwater conditions.

Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Multi-camera systems are essential in movies, live broadcasts, and other media. The selection of the appropriate camera for every moment has a decisive impact on production quality and audience preferences. Learning-based multi-camera view recommendation frameworks have been explored to assist professionals in decision making. This work explores how two standard cinematography practices could be incorporated into the learning pipeline: (1) not staying on the same camera for too long and (2) introducing a scene from a wider shot and gradually progressing to narrower ones. In these regards, we incorporate (1) the duration of the displaying camera and (2) camera identity as temporal and camera embedding in a transformer architecture, thereby implicitly guiding the model to learn the two practices from professional-labeled data. Experiments show that the proposed framework outperforms the baseline by 14.68% in six-way classification accuracy. Ablation studies on different approaches to embedding the temporal and camera information further verify the efficacy of the framework.

We present Anywhere Avatar, a telepresence system that enables full-body and facial avatar reconstruction using a smartphone and a laptop. Users record short videos to generate personalized avatars, which are animated in real time during teleconferencing using webcam-based tracking. Built on pre-trained FLAME and SMPL models, the avatars are rendered in high fidelity using Gaussian splatting. The system runs at near real-time with minimal bandwidth, making expressive 3D telepresence accessible without specialized hardware.

Offering high-quality immersive content is the ultimate goal of volumetric video streaming. Although point clouds and meshes are dominant volumetric representations, their limitations in depicting photo-realistic content often undermine user experience. The recent advent of neural radiance fields (NeRF) offers a promising alternative content representation with superior photo-realism. However, streaming NeRF-based volumetric videos over wireless networks to mobile headsets faces significant challenges, including substantial bandwidth usage because of the large frame size, degraded visual quality due to even a low packet loss rate, and content artifacts caused by performance optimizations (e.g., remote rendering at the network edge). To address these challenges, in this paper, we introduce NeVo, a next-generation volumetric video streaming system for efficient delivery of neural content such as NeRF. NeVo incorporates the following innovations into a holistic system: (1) a novel method to model visibility of implicitly encoded neural content, thereby avoiding non-essential transmission to drastically reduce network data usage, (2) a lightweight, learning-based model for real-time content reconstruction after packet loss with carefully chosen data, and (3) judicious identification and selective delivery of intermediate data in edge-based NeRF rendering to effectively mitigate artifacts. Our extensive experiments indicate that compared with the state-of-the-art, NeVo saves up to 68.3% of bandwidth usage, maintains high visual quality despite packet loss, and enhances user experience by reducing artifacts.

Modern AI systems struggle most in environments where reliability is critical - scenes with smoke, poor visibility, and structural deformation. Each year, tens of thousands of firefighters are injured on duty, often due to breakdowns in situational perception. We introduce Fire360, a benchmark for evaluating perception and reasoning in safety-critical firefighting scenarios. The dataset includes 228 360° videos from professional training sessions under diverse conditions (e.g., low light, thermal distortion), annotated with action segments, object locations, and degradation metadata. Fire360 supports five tasks: Visual Question Answering, Temporal Action Captioning, Object Localization, Safety-Critical Reasoning, and Transformed Object Retrieval (TOR). TOR tests whether models can match pristine exemplars to fire-damaged counterparts in unpaired scenes, evaluating episodic memory under irreversible visual transformations. While human experts achieve 83.5% on TOR, models like GPT-4o lag significantly, exposing failures in reasoning under degradation. By releasing Fire360 and its evaluation suite, we aim to advance models that not only see, but also remember, reason, and act under uncertainty. The dataset is available at https://uofi.box.com/v/fire360dataset

Video processing for real-time analytics in resource-constrained environments presents a significant challenge in balancing energy consumption and video semantics. This paper addresses the problem of energy-efficient video processing by proposing a system that dynamically optimizes processing configurations to minimize energy usage on the edge, while preserving essential video features for deep learning inference. We first gather an extensive offline profile of various configurations consisting of device CPU frequencies, frame filtering features, difference thresholds, and video bitrates, to establish apriori knowledge of their impact on energy consumption and inference accuracy. Leveraging this insight, we introduce an online system that employs multi-objective Bayesian optimization to intelligently explore and adapt configurations in real time. Our approach continuously refines processing settings to meet a target inference accuracy with minimal edge device energy expenditure. Experimental results demonstrate the system's effectiveness in reducing video processing energy use while maintaining high analytical performance, offering a practical solution for smart devices and edge computing applications.

Effective training and debriefing are critical in high-stakes, mission-critical environments such as firefighting, where precision and error minimization are paramount. The traditional post-training analysis relies on the manual review of 2D video, a process that is time-consuming and lacks comprehensive situational awareness. To address these limitations, we introduce ACT360, a novel system that leverages 360-degree video and machine learning for automated action detection and efficient debriefing. ACT360 incorporates 360YOWO, a customized You Only Watch Once (YOWO) model enhanced with a spatial attention mechanism and equirectangular-aware convolution (EAC) to handle the unique distortions of panoramic video data. To enable deployment in resource-constrained environments, we apply quantization and model pruning, reducing the model size by 74% while maintaining robust accuracy (mAP drop of only 1.5 %, from 0.865 to 0.850) and improving inference speed. We validate our approach on a new, publicly available dataset of 55 labeled 360-degree videos covering seven key firefighting actions, recorded across various real-world practice sessions and environmental conditions. Furthermore, we integrate the pipeline with 360AIE (Action Insight Explorer), a web-based interface that provides automatic action detection, retrieval, and textual summarization of key events using large language models (LLMs), significantly improving post-incident analysis efficiency. ACT360 serves as a generalized framework for mission-critical debriefing, incorporating techniques such as EAC, spatial attention, summarization, and model optimization. These innovations apply to any training environment requiring lightweight action detection and structured nost-exercise analysis.

Deep reinforcement learning (DRL) demonstrates its promising potential in adaptive video streaming and has recently received increasing attention. However, existing DRL-based methods for adaptive video streaming mainly use application (APP) layer information, adopt heuristic training methods, and are not robust against continuous network fluctuations. This paper aims to boost the quality of experience (QoE) of adaptive wireless video streaming by using cross-layer information, deriving a rigorous training method, and adopting effective online tuning methods with real-time data. First, we formulate a more comprehensive and accurate adaptive wireless video streaming problem as an infinite stage discounted Markov decision process (MDP) problem by additionally incorporating past and lower-layer information. This formulation allows a flexible tradeoff between QoE and computational and memory costs for solving the problem. In the offline scenario (only with pre-collected data), we propose an enhanced asynchronous advantage actor-critic (eA3C) method by jointly optimizing the parameters of parameterized policy and value function. Specifically, we build an eA3C network consisting of a policy network and a value network that can utilize cross-layer, past, and current information and jointly train the eA3C network using pre-collected samples. In the online scenario (with additional real-time data), we propose two continual learning-based online tuning methods for designing better policies for a specific user with different QoE and training time tradeoffs. The proposed online tuning methods are robust against continuous network fluctuations and more general and flexible than the existing online tuning methods. Finally, experimental results show that the proposed offline policy can improve the QoE by 6.8% to 14.4% compared to the state-of-the-arts in the offline scenario, and the proposed online policies can achieve$6.3\%$to 55.8% gains in QoE over the state-of-the-arts in the online scenario.

Recent advances in computer vision algorithms and video streaming technologies have facilitated the development of edge-server-based video analytics systems, enabling them to process sophisticated real-world tasks, such as traffic surveillance and workspace monitoring. Meanwhile, due to their omnidirectional recording capability, 360-degree cameras have been proposed to replace traditional cameras in video analytics systems to offer enhanced situational awareness. Yet, we found that providing an efficient 360-degree video analytics framework is a non-trivial task. Due to the higher resolution and geometric distortion in 360-degree videos, existing video analytics pipelines fail to meet the performance requirements for end-to-end latency and query accuracy. To address these challenges, we introduce the innovative ST-360 framework specifically designed for 360-degree video analytics. This framework features a spatial–temporal filtering algorithm that optimizes both data transmission and computational workloads. Evaluation of the ST-360 framework on a unique dataset of 360-degree first-responders videos reveals that it yields accurate query results with a 50% reduction in end-to-end latency compared to state-of-the-art methods.

Xinyu Pi, Mingyuan Wu, Jize Jiang, Haozhen Zheng, Beitong Tian, ChengXiang Zhai, Klara Nahrstedt, Zhiting Hu. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise. However, the straggler issue, due to slow clients, often hinders the efficiency and scalability of FL. This paper presents FedCore, an algorithm that innovatively tackles the straggler problem via the decentralized selection of coresets, representative subsets of a dataset. Contrary to existing centralized coreset methods, FedCore creates coresets directly on each client in a distributed manner, ensuring privacy preservation in FL. FedCore translates the coreset optimization problem into a more tractable k-medoids clustering problem and operates distributedly on each client. Theoretical analysis confirms FedCore's convergence, and practical evaluations demonstrate an 8x reduction in FL training time, without compromising model accuracy. Our extensive evaluations also show that FedCore generalizes well to existing FL frameworks11Code: https://github.com/hongpeng-guo/PedCore.

Deep reinforcement learning (DRL) demonstrates its promising potential in the realm of adaptive video streaming. However, existing DRL-based methods for adaptive video streaming use only application (APP) layer information and adopt heuristic training methods. This paper aims to boost the quality of experience (QoE) of adaptive wireless video streaming by using lower-layer information and deriving a rigorous training method. First, we formulate a more comprehensive and accurate adaptive wireless video streaming problem as an infinite stage discounted Markov decision process (MDP) problem by additionally incorporating past and lower-layer information, allowing a flexible tradeoff between QoE and computational and memory costs for solving the problem. Then, we propose an enhanced asynchronous advantage actor-critic (eA3C) method by jointly optimizing the parameters of parameterized policy and value function. Specifically, we build an eA3C network consisting of a policy network and a value network that can utilize cross-layer, past, and current information and jointly train the eA3C network using pre-collected samples. Finally, experimental results show that the proposed eA3C method can improve the QoE by 6.8%$\sim$14.4% compared to the state-of-the-arts.

Image matting has become an essential functionality of image capturing and editing tools. While trimap and scribble-based techniques have shown notable success in these applications, generating high-quality alpha mattes without trimap inputs remains challenging. Existing trimap-free methods divide the task into coarse semantic mask prediction and detailed matte prediction, and an optimization is formulated by balancing these two tasks. However, emphasizing the optimization of the coarse mask leads to inaccurate matte, and emphasizing the optimization of the detailed matte leads to degraded semantic integrity or background artifacts. In this paper, we propose an improved trimap-free training strategy (I-Matting) that effectively ensures semantic integrity, removes background artifacts, and improves local details. First, we introduce two discriminators to distinguish the matting outputs versus the ground truths, which boosts the semantic without hurting the matte prediction. Second, a novel patch-rank module is proposed to improve the matting accuracy by leveraging high-resolution inputs, without hurting the semantic integrity. Meanwhile, the accuracy gain produced by I-Matting is not at the expense of any additional cost in the inference. Extensive experiments show that our method significantly outperforms existing approaches.

Video processing is becoming increasingly popular and cost-effective on IoT devices but faces challenges in transmitting data under varying timing constraints and network bandwidth. Existing compression methods struggle with incomplete data. We present FrameCorr, a deep learning framework that leverages prior-received video data to predict and reconstruct missing frame segments, enabling video reconstruction despite data loss.

We propose an interactive and intelligent hybrid teleconferencing system compatible with Virtual Reality devices. Our system understands meeting contexts and leverages user interactions to enhance better system configuration. Employing interactive scene graphs [11], the system extracts and transmits essential meeting context to users while relaying user interactions back to the streaming systems for user-involved adaptive streaming and foveated rendering. We demonstrate the system's real-time performance and compatibility with commercial VR devices such as the Meta Quest 3.

Video codecs are crucial in video streaming systems. However, the quantization operation in existing codecs introduces irreversible jitters. Moreover, the common practice of fitting a single codec to diverse video content lacks the flexibility to adapt the parameters of a codec for specific content. They lead to the problem of quantization and content uncertainty. Our preliminary study shows an ideal codec without uncertainty gains a significant advantage over the conventional codec with uncertainty. However, realizing the ideal codec presents tremendous challenges in the generalizability and the costs of computation, transmission, and delay. In this paper, we present Vesper, a video streaming system that innovatively tackles uncertainty with two learning-based components, super-precision and self-evolution. The super-precision module builds a neural network that predicts original feature values from quantized feature values, which effectively mitigates the impact of quantization without inducing generalizability issues. The self-evolution module performs content-aware adaptation on the encoder and replaces non-content-aware video segments with content-aware ones on the fly, which addresses content uncertainty without adding significant costs to on-demand streaming. Evaluations demonstrate Vesper's superior Quality of Experience compared to streaming systems built with state-of-the-art codecs.

Neural Radiance Fields (NeRF) are recognized for their exceptional photo-realism quality and superior modeling capabilities compared to traditional methods. NeRF empowers a novel application, termed NeRF serving. It delivers data from a server to a mobile client and renders 3D scenes on the client, facilitating a broad spectrum of mobile immersive applications. Towards a satisfactory user experience, we must serve NeRF with low latency while meeting constraints of high visual quality and real-time smoothness. Existing NeRF variants easily violate the constraints or cause an unnecessarily high latency when the diverse applications, mobile devices, and 3D scenes, termed the contexts, change in real life. In this paper, we present NeRFHub, a novel context-aware NeRF serving framework for mobile immersive applications. NeRFHub adeptly manages storage and computation costs, scales to diverse contexts, and swiftly navigates the vast design space inherent in NeRF serving. The evaluation results show that NeRFHub serves synthetic objects with 56%-66% reduced latency and realistic scenes with 26%-55% reduced latency when compared to the baseline without compromising quality or smoothness.

The multi-camera capture system is an emerging visual sensing modality. It facilitates the production of various immersive contents ranging from regular to neural videos. Although the delivery of immersive content is popular and promising, it suffers from the bandwidth bottleneck when streaming multi-view videos to the cloud (i.e., multi-view video aggregation). Existing works fail to provide a bandwidth-efficient and content-generic solution. Even the closest effort to ours based on the SOTA multi-view video codecs suffers from issues of underutilized dependency and content distortion. In this paper, we present ImmerScope, a multi-view video aggregation framework at the edge with a neural multi-view video codec. It outperforms existing solutions with highly-utilized dependency via neuron connections and distortion awareness via end-to-end training. Evaluations on diverse multi-camera setups show that ImmerScope outperforms single-view codecs by at least 64% bandwidth savings in peak-signal-to-noise ratio with a frame rate of 50 fps.

Multi-camera systems are indispensable in movies, TV shows, and other media. Selecting the appropriate camera at every timestamp has a decisive impact on production quality and audience preferences. Learning-based view recommendation frameworks can assist professionals in decision-making. However, they often struggle outside of their training domains. The scarcity of labeled multi-camera view recommendation datasets exacerbates the issue. Based on the insight that many videos are edited from the original multi-camera videos, we propose transforming regular videos into pseudo-labeled multi-camera view recommendation datasets. Promisingly, by training the model on pseudo-labeled datasets stemming from videos in the target domain, we achieve a 68% relative improvement in the model’s accuracy in the target domain and bridge the accuracy gap between in-domain and never-before-seen domains.

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression which minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standards and traditional image compression techniques do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth tradeoff, they introduce excessive latency in the visual analytics offloading pipeline. This article presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the tradeoff between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under comparable analytics accuracy. Regarding the low-latency support, CICO achieves up to a 2× speedup over state-of-the-art compression techniques.

In a real-life meeting environment, individuals often demonstrate a remarkable ability to selectively focus their attention on specific visual information. This ability allows them to naturally concentrate on a specific region of interest while tuning out others. Understanding and exploiting such selective attention remains unexplored in a user-centric teleconferencing system, where there is a potential to customize video streaming and foveated rendering based on the viewer’s attention. This paper proposes a novel user-centric scene analysis module that fully leverages the power of selective attention for online meeting scenarios and recognizes the unequal importance of individual pixels in the videos. The module determines the user’s selective attention through the meeting contexts. The contextual representation of the meeting is modeled as a combination of two primary components: proactive user interaction within the system and passive real-time analysis of high-level visual semantics from the scenes. As the meeting progresses, the interactive scene analysis module dynamically updates its contextual representation, offering a dual advantage: (a) Videos can be selectively and adaptively streamed within a user’s attention, resulting in bandwidth savings of up to 78 percent. (b) The module enhances the overall quality of the user experience by facilitating higher user interactivity, particularly in meeting-related tasks such as screen sharing, privacy-preserving user blocking, background removal, automatic user attention shift detection, etc. Our interactive scene analysis module makes significant progress toward enabling an efficient, immersive, and intelligent teleconferencing system.

The emergence of 360-video streaming systems has brought about new possibilities for immersive video experiences while requiring significantly higher bandwidth than traditional 2D video streaming. Viewport prediction is used to address this problem, but interesting storylines outside the viewport are ignored. To address this limitation, we present SAVG360, a novel viewport guidance system that utilizes global content information available on the server side to enhance streaming with the best saliency-captured storyline of 360-videos. The saliency analysis is performed offline on the media server with powerful GPU, and the saliency-aware guidance information is encoded and shared with clients through the Saliency-aware Guidance Descriptor. This enables the system to proactively guide users to switch between storylines of the video and allow users to follow or break guided storylines through a novel user interface. Additionally, we present a viewing mode prediction algorithms to enhance video delivery in SAVG360. Evaluation of user viewport traces in 360-videos demonstrate that SAVG360 outperforms existing tiled streaming solutions in terms of overall viewport prediction accuracy and the ability to stream high-quality 360 videos under bandwidth constraints. Furthermore, a user study highlights the advantages of our proactive guidance approach over predicting and streaming of where users look.

360-degree video has become increasingly popular in content consumption. However, finding the viewing direction for important content within each frame poses a significant challenge. Existing approaches rely on either viewer input or algorithmic determination to select the viewing direction, but neither mode consistently outperforms the other in terms of content-importance. In this paper, we propose 360TripleView, the first view management system for 360-degree video that automatically infers and utilizes the better view mode for each frame, ultimately providing viewers with higher content-importance views. Through extensive experiments and a user study, we demonstrate that 360TripleView achieves over 90% accuracy in inferring the better mode and significantly enhances content-importance compared to existing methods.

First responders operate in hazardous working conditions with unpredictable risks. To better prepare for demands of the job, first responder trainees conduct training exercises that are being recorded and reviewed by the instructors, who check for objects indicating risks within the video recordings (e.g., firefighter with an unfastened gas mask). However, the traditional reviewing process is inefficient due to unanalyzed video recordings and limited situational awareness. For better reviewing experience, a latency-aware Viewing and Query Service (VQS) should be provided. The VQS should support object searching, which can be achieved using the video object detection algorithms. Meanwhile, the application of 360-degree cameras facilitates an unlimited field of view of the training environment. Yet, this medium represents a major challenge because low-latency high-accuracy 360-degree object detection is difficult due to higher resolution and geometric distortion. In this paper, we present the Responders-360 system architecture designed for 360-degree object detection. We propose a Dynamic Selection algorithm that optimizes computation resources while yielding accurate 360-degree object inference. The results, using a unique dataset collected from a firefighting training institute, show that the Responders-360 framework achieves 4x speedup and 25% memory usage reduction compared with the state-of-the-art methods.

In a 5G testbed, we use 360° video streaming to test, measure, and demonstrate the 5G infrastructure, including the capabilities and challenges of edge computing support. Specifically, we use the SEAWARE (Semantic-Aware View Prediction) software system, originally described in [1], at the edge of the 5G network to support a 360° video player (handling tiled videos) by view prediction. Originally, SEAWARE performs semantic analysis of a 360° video on the media server, by extracting, e.g., important objects and events. This video semantic information is encoded in specific data structures and shared with the client in a DASH streaming framework. Making use of these data structures, the client/player can perform view prediction without in-depth, computationally expensive semantic video analysis. In this paper, the SEAWARE system was ported and adapted to run (partially) on the edge where it can be used to predict views and prefetch predicted segments/tiles in high quality in order to have them available close to the client when requested. The paper gives an overview of the 5G testbed, the overall architecture, and the implementation of SEAWARE at the edge server. Since an important goal of this work is to achieve low motion-to-glass latencies, we developed and describe "tile postloading", a technique that allows non-predicted tiles to be fetched in high quality into a segment already available in the player buffer. The performance of 360° tiled video playback on the 5G infrastructure is evaluated and presented. Current limitations of the 5G network in use and some challenges of DASH-based streaming and of edge-assisted viewport prediction under "real-world" constraints are pointed out; further, the performance benefits of tile postloading are disclosed.

Federated learning (FL) is a machine learning paradigm that enables a cluster of decentralized edge devices to collaboratively train a shared machine learning model without exposing users' raw data. However, the intensive model training computation is energy-demanding and poses severe challenges to end devices' battery life. In this paper, we present BoFL, a training pace controller deployed on the edge devices that actuates the hardware operational frequencies over multiple configurations to achieve energy-efficient federated learning. BoFL operates in an explore-then-exploit manner within limited rounds of FL tasks. BoFL explores the large hardware frequency space strategically with a tailor-designed Bayesian optimization algorithm. BoFL first finds a set of good operational configurations within few task training rounds, and then exploits these configurations in the remaining rounds to achieve minimized energy consumption for model training. Experiments on multiple real-world edge devices with different FL tasks suggest that BoFL can reduce energy consumption of model training by around 26%, and achieve near-optimal energy efficiency.

360-degree video is becoming an integral part of our content consumption through both video on demand and live broadcast services. However, live broadcast is still challenging due to the huge network bandwidth cost if all 360-degree views are delivered to a large viewer population over diverse networks. In this paper, we present 360BroadView, a viewer management approach to viewport prediction in 360-degree video live broadcast. We make some high-bandwidth network viewers be leading viewers to help the others (lagging viewers) predict viewports during 360-degree video viewing and save bandwidth. Our viewer management maintains the leading viewer population despite viewer churns during live broadcast, so that the system keeps functioning properly. Our evaluation shows that 360BroadView maintains the leading viewer population at a minimal yet necessary level for 97 percent of the time.

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression that minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standard and traditional image compression do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth trade-off, they introduce excessive latency in the visual analytics offloading pipeline. This paper presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the trade-off between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under a comparable analytics accuracy. In terms of the low-latency support, CICO achieves up to a 2x speedup over state-of-the-art compression techniques.

While 360° videos are gaining popularity due to the emergence of VR technologies, storing and streaming such videos can incur up to 20X higher overheads than traditional HD content. Edge caching, which involves caching and serving 360° videos from edge servers, is one possible approach for addressing these overheads. Prior work on 360° video caching has been based on using past history to cache tiles that are likely to be in a viewer's field of view and has not considered methods to intelligently share a limited edge cache across a set of videos that exhibit large variations in their popularity, size, content, and user abandonment patterns. Towards this end, we present CAVE, an adaptive edge caching framework that intelligently optimizes cache allocation across a set of videos taking into account video content, size, and popularity. Our experiments using realistic video workloads shows CAVE improves cache hit-rates, and thus network saving, by up to 50% over state-of-the-art approaches, while also scaling to up to two thousand videos per edge cache. In addition, in terms of scalability, our developed algorithm is embarrassingly parallel, allowing CAVE to scale beyond state-of-the-art solutions that typically do not support parallelization.

In recent years, streamed 360° videos have gained popularity within Virtual Reality (VR) and Augmented Reality (AR) applications. However, they are of much higher resolutions than 2D videos, causing greater bandwidth consumption when streamed. This increased bandwidth utilization puts tremendous strain on the network capacity of the cloud providers streaming these videos. In this paper, we introduce L3BOU, a novel, three-tier distributed software framework that reduces cloud-edge bandwidth in the backhaul network and lowers average end-to-end latency for 360° video streaming applications. The L3BOU framework achieves low bandwidth and low latency by leveraging edge-based, optimized upscaling techniques. L3BOU accomplishes this by utilizing down-scaled MPEG-DASH-encoded 360° video data, known as Ultra Low Resolution (ULR) data, that the L3BOU edge applies distributed super-resolution (SR) techniques on, providing a high quality video to the client. L3BOU is able to reduce the cloud-edge backhaul bandwidth by up to a factor of 24, and the optimized super-resolution multi-processing of ULR data provides a 10-fold latency decrease in super resolution upscaling at the edge.

Immersive virtual tours based on 360-degree cameras, showing famous outdoor scenery, are becoming more and more desirable due to travel costs, pandemics and other constraints. To feel immersive, a user must receive the view accurately corresponding to her position and orientation in the virtual space when she moves inside, and this requires cameras’ orientations to be known. Outdoor tour contexts have numerous, ultra-sparse cameras deployed across a wide area, making camera pose estimation challenging. As a result, pose estimation techniques like SLAM, which require mobile or dense cameras, are not applicable. In this paper we present a novel strategy called 360ViewPET, which automatically estimates the relative poses of two stationary, ultra-sparse (15 meters apart) 360-degree cameras using one equirectangular image taken by each camera. Our experiments show that it achieves accurate pose estimation, with a mean error as low as 0.9 degree.

Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate.

Spatio-temporal action localization aims to detect the spatial location and the start/end time of the action in a video. The state-of-the-art approach uses convolutional neural networks to extract possible bounding boxes for the action in each frame and then link bounding boxes into action tubes based on the location and the class-specific score of each bounding box. Though this approach has been successful at achieving a good localization accuracy, it is computation-intensive. High-end GPUs are usually demanded for it to achieve real-time performance. In addition, this approach does not scale well on a large number of action classes. In this work, we present a framework, EScALation, for making spatio-temporal action localization efficient and scalable. Our framework involves two main strategies. One is the frame sampling technique that utilizes the temporal correlation between frames and selects key frame(s) from a temporally correlated set of frames to perform bounding box detection. The other is the class filtering technique that exploits bounding box information to predict the action class prior to linking bounding boxes. We compare EScALation with the state-of-the-art approach on UCF101-24 and J-HMDB-21 datasets. One of our experiments shows EScALation is able to save 72.2% of the time with only 6.1% loss of mAP. In addition, we show that EScALation scales better to a large number of action classes than the state-of-the-art approach.

Video cameras are pervasively deployed in city scale for public good or community safety (i.e. traffic monitoring or suspected person tracking). However, analyzing large scale video feeds in real time is data intensive and poses severe challenges to today's network and computation systems. We present CrossRoI, a resource-efficient system that enables real time video analytics at scale via harnessing the videos content associations and redundancy across a fleet of cameras. CrossRoI exploits the intrinsic physical correlations of cross-camera viewing fields to drastically reduce the communication and computation costs. CrossRoI removes the repentant appearances of same objects in multiple cameras without harming comprehensive coverage of the scene. CrossRoI operates in two phases - an offline phase to establish cross-camera correlations, and an efficient online phase for real time video inference. Experiments on real-world video feeds show that CrossRoI achieves 42% ~ 65% reduction for network overhead and 25% ~ 34% reduction for response delay in real time video analytics applications with more than 99% query accuracy, when compared to baseline methods. If integrated with SotA frame filtering systems, the performance gains of CrossRoI reaches 50% ~ 80% (network overhead) and 33% ~ 61% (end-to-end delay).

Recent years have witnessed sensors becoming an indispensable part of our life with the camera being one of the most popular and widely deployed sensors. The camera gives rise to numerous vision-based IoT applications that generate high-level understandings of a live video stream by performing analysis on end devices like mobile or embedded devices. Typically, these applications are built with deep learning (DL) models to conduct complex vision tasks, e.g., image classification and object detection. Due to the prohibitive cost of running DL models on end devices close to the camera and with limited computation capabilities, it is widely adopted to offload the computation to a nearby powerful edge server. However, there is a gap between the restricted offloading bandwidth of the end device and the large volume of image data incurred by the live video stream. In this paper, we present Deep Contextualized Compressive Offloading for Images (DCCOI), a lightweight, context-aware, and bandwidth-efficient offloading framework for images. DCCOI consists of the spatial-adaptive encoder, a lightweight neural network, to spatial-adaptively compress the image, and the generative decoder for reconstructing the image from the compressed data. In contrast to existing DL-based encoders, the spatial-adaptive encoder allows an image region to be encoded into different numbers of feature values based on the information in it. This offers a variable-length coding method for image compression, which is a more optimal way for compression than the fix-length coding method took by existing DL-based compression approaches and demonstrates superior accuracy-compression rate trade-offs. We evaluate DCCOI against several baseline compression techniques while serving an object detection-based application. The results show that DCCOI roughly reduces the offloading size of JPEG by a factor of 9 and DeepCOD, the state-of-the-art offloading approach, by 20% with similar accuracy and a compression overhead less than 50ms.

Recent advances in Deep Neural Networks (DNN) and Edge Computing have made it possible to automatically analyze streams of videos from home/security cameras over hierarchical clusters that include edge devices, close to the video source, as well as remote cloud compute resources. However, preserving the privacy and confidentiality of users' sensitive data as it passes through different devices remains a concern to most users. Private user data is subject to attacks by malicious attackers or misuse by internal administrators who may use the data in activities that are not explicitly approved by the user. To address this challenge, we present Serdab, a distributed orchestration framework for deploying deep neural network computation across multiple secure enclaves (e.g., Intel SGX). Secure enclaves provide a guarantee on the privacy of the data/code deployed inside it. However, their limited hardware resources make them inefficient when solely running an entire deep neural network. To bridge this gap, Serdab presents a DNN partitioning strategy to distribute the layers of the neural network across multiple enclave devices or across an enclave device and other hardware accelerators. Our partitioning strategy achieves up to 4.7x speedup compared to executing the entire neural network in one enclave.

The technology advancement has made Unmanned Aerial Vehicle (UAV) swarm a promising method to achieve complicated missions that a single UAV cannot support. Leader-followers formation is a widely used swarm management scenario where a leader drone frequently broadcasts controlling messages to all follower drones to achieve collaboratively a common mission. However, managing such a UAV swarm, especially when the member drones dynamically join and leave the swarm, introduces significant security challenges and performance overhead.In this work, we propose a Swarm Broadcast Protocol (SBP) to facilitate the security protection of leader-followers formation based UAV swarms. SBP contains a security key management scheme that manages a broadcast key among the swarm for leader to broadcast encrypted messages to followers. When swarm membership changes, the broadcast key will be updated and synchronized among the swarm to maintain both backward and forward secrecy. The overhead of SBP is small that only constant computational overhead is needed for both swarm leader and followers to achieve key synchronization when a new drone joins regardless of the current swarm size. This feature would highly reduce the overhead when there are many individual drone joining events. Through experiments on network emulator, we show that SBP achieves lowest bandwidth overhead and CPU utilization to handle multiple swarm membership changing events, comparing with two public-key-based swarm management protocol baselines.

Recent advances in computer vision and neural networks have made it possible for more surveillance videos to be automatically searched and analyzed by algorithms rather than humans. This happened in parallel with advances in edge computing where videos are analyzed over hierarchical clusters that contain edge devices, close to the video source. However, the current video analysis pipeline has several disadvantages when dealing with such advances. For example, video encoders have been designed for a long time to please human viewers and be agnostic of the downstream analysis task (e.g., object detection). Moreover, most of the video analytics systems leverage 2-tier architecture where the encoded video is sent to either a remote cloud or a private edge server but does not efficiently leverage both of them. In response to these advances, we present SIEVE, a 3-tier video analytics system to reduce the latency and increase the throughput of analytics over video streams. In SIEVE, we present a novel technique to detect objects in compressed video streams. We refer to this technique as semantic video encoding because it allows video encoders to be aware of the semantics of the downstream task (e.g., object detection). Our results show that by leveraging semantic video encoding, we achieve close to 100% object detection accuracy with decompressing only 3.5% of the video frames which results in more than 100x speedup compared to classical approaches that decompress every video frame.

Spatio-temporal action localization of human actions in a video has been a popular topic over the past few years. It tries to localize the bounding boxes, the time span and the class of one action, which summarizes information in the video and helps humans understand it. Though many approaches have been proposed to solve this problem, these efforts have only focused on perspective videos. Unfortunately, perspective videos only cover a small field-of-view (FOV), which limits the capability of action localization. In this paper, we develop a comprehensive approach to real-time spatio-temporal localization that can be used to detect actions in 360 videos. We create two datasets named UCF-101-24-360 and JHMDB-21-360 for our evaluation. Our experiments show that our method consistently outperforms other competing approaches and achieves a real-time processing speed of 15fps for 360 videos.

360-degree video has been popular due to the immersive experience it provides to the viewer. While watching, viewer can control the field of view (FoV) in the range of 360° by 180°. As this trend continues, adaptive bitrate (ABR) streaming is becoming a prevalent issue. Most existing ABR algorithms for 360 videos (360 ABR algorithms) require real-time head traces and certain computation resource from the client for streaming, which largely constrains the range of audience. Also, while more 360 ABR algorithms rely upon machine learning (ML) for viewport prediction, ML and ABR are research topics that grow mostly independently. In this paper, we use viewport interchangeably with FoV in the range of 360° by 180°. As this trend continues, adaptive bitrate (ABR) streaming is becoming a prevalent issue. Most existing ABR algorithms for 360 videos (360 ABR algorithms) require real-time head traces and certain computation resource from the client for streaming, which largely constrains the range of audience. Also, while more 360 ABR algorithms rely upon machine learning (ML) for viewport prediction, ML and ABR are research topics that grow mostly independently. In this paper, we propose a two-fold ABR algorithm for 360 video streaming that utilizes 1) an off-the-shelf ABR algorithm for ordinary videos, and 2) an off-the-shelf viewport prediction model. Our algorithm requires neither real-time head traces nor additional computation from the viewing device. In addition, it adapts easily to the newest developments in viewport prediction and ABR. As a consequence, the proposed method fits nicely to the existing streaming framework and any advancement in viewport prediction and ABR could enhance its performance. With the quantitative experiments, we demonstrate that the proposed method achieves twice the quality of experience (QoE) compared to the baseline.

Future view prediction for a 360-degree video streaming system is important to save the network bandwidth and improve the Quality of Experience (QoE). Historical view data of a single viewer and multiple viewers have been used for future view prediction. Video semantic information is also useful to predict the viewer's future behavior. However, extracting video semantic information requires powerful computing hardware and large memory space to perform deep learning-based video analysis. It is not a desirable condition for most of client devices, such as small mobile devices or Head Mounted Display (HMD). Therefore, we develop an approach where video semantic analysis is executed on the media server, and the analysis results are shared with clients via the Semantic Flow Descriptor (SFD) and View-Object State Machine (VOSM). SFD and VOSM become new descriptive additions of the Media Presentation Description (MPD) and Spatial Relation Description (SRD) to support 360-degree video streaming. Using the semantic-based approach, we design the Semantic-Aware View Prediction System (SEAWARE) to improve the overall view prediction performance. The evaluation results of 360-degree videos and real HMD view traces show that the SEAWARE system improves the view prediction performance and streams high-quality video with limited network bandwidth.

Theworld iswelcoming the newnormal - the coronavirus pandemic has significantly changed the way people live, work, communicate and learn. Almost everyone now is wearing a face mask when they go in public. People are working from home, some taking care of children at the same time. Bars and restaurants are limited to carry-out and delivery only. Meetings and conferences go online. Schools are closed and educators are instead holding video conference classes regularly. All these become the new normal as our ways of life. The panel thus provides a valuable opportunity for people from a variety of backgrounds to exchange views on opportunities and challenges for AI multimedia in the current and post pandemics era.

With the emergence of new 360-degree cameras, ambisonic microphones, and VR/AR display devices, more diverse multi-modal content has become available, and with it the demand for the capability of streaming 360-degree videos to enhance users? 360-multimedia experience on mobile devices such as mobile phones and head-mounted displays. The big issue for the mobile 360-multimedia delivery systems is the huge resource demand on the underlying networks and devices to deliver 360-multimedia content with high quality of experience. In this talk, we will discuss the research challenges of 360-degree video delivery systems such as the large bandwidth, low latency, users? disorientation, and cyber-sickness, and opportunities to solve these challenges including rate adaptation algorithms of tiles videos, view prediction algorithms, content navigation, enhancement of DASH streaming for 360-videos, and control of Quality of Experience (QoE) [1]. We will briefly dive into more details of the concept of navigation graphs for 360-degree videos and present the opportunity of navigation graphs to organize 360-video content that can help in viewing navigation, caching and improvements of QoE [2]. We will show how navigation graphs are serving as models for viewing behaviors in the temporal and spatial domains, and can assist with view predictions, bandwidth, and latency control. Our experimental results are encouraging [3] and support the intuition that if we can encapsulate viewing patterns of 360-degree videos into navigation graphs at multiple levels of contextual details, we will be able to stream "need-to-see" 360-content to wireless HMD devices in timely manner within bandwidth-constrained environments, and enhance viewing quality experience of 360-degree videos in augmented reality applications.

We demonstrate a video 360 navigation and streaming system for Mobile HMD devices. The Navigation Graph (NG) concept is used to predict future views that use a graph model that captures both temporal and spatial viewing behavior of prior viewers. Visualization of video 360 content navigation and view prediction algorithms is used for assessment of Quality of Experience (QoE) and evaluation of the accuracy of the NG-based view prediction algorithm.

Unmanned Aerial Vehicles (UAVs) are becoming more popular than ever in outdoor commercial services. Many third-party UAV companies offer their UAVs as mobile data collectors to assist their clients in remote data collection missions. However, due to the lack of trust and transparency, the clients often have very little control on the behavior of these UAVs. This issue is even exacerbated if the service had to deal with private client data. In this work, we propose a solution that enables third-party UAVs to collect and process private client data from remote data sites in a trustworthy and efficient manner. We design and implement the Secure Homomorphic Encryption (SHE) framework. SHE combines trusted hardware enclave and homomorphic encryption technologies to provide strong privacy primitives on client data. SHE features in a recrypt technique such that the computation and communication overhead for homomorphic encryption on the client data is minimized. In addition, SHE takes the advantage of UAVs' travelling time to run data aggregation tasks in order to speed-up data processing. Through laboratory experiments, we demonstrate that SHE can meet the performance requirement in many common data processing and aggregation missions. SHE thus can be introduced as a trustworthy framework for the third-party UAV service providers.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smartphone, touching the desk used by the victim to record the audio signals of handwriting. Then, the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering, dictionary filtering and letter time length based offsetting. Moreover, if the relative position between the device and the handwriting is known, a hand motion tracking method can be further applied to enhance the system's performance. Our prototype system's experimental results show that the accuracy of word recognition reaches around 70 - 80 percent under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems, where a myriad of data requesters outsource their sensing tasks to a crowd of workers via a cloud-based platform. In order to incentivize participation, requesters typically compensate workers with specific amount of payments. Clearly, setting an appropriate task price is critical for a requester to attract enough worker participation without unnecessary expenses. Therefore, we investigate the problem of task pricing in MCS systems with multi-requester price competition, and also dynamically arriving workers. Task pricing in such scenario is challenging, because of each requester's incomplete information about the others, uncertainty of future information, etc. So as to address these challenges, we use Markov game to model requesters' competitive task pricing, and Markov correlated equilibrium (MCE) as the solution concept. We propose that the platform uses the social cost-minimizing MCE to coordinate requesters' prices, which is self-enforcing, and optimizes the system-wide objective of social cost. Technically, we propose a computationally efficient algorithm to compute an approximately optimal MCE. Furthermore, through extensive performance evaluation, we show numerically that our algorithm yields close-to-minimum social cost in very short running time.

After the emergence of video streaming services, more creative and diverse multimedia content has become available, and now the capability of streaming 360-degree videos will open a new era of multimedia experiences. However, streaming these videos requires larger bandwidth and less latency than what is found in conventional video streaming systems. Rate adaptation of tiled videos and view prediction techniques are used to solve this problem. In this paper, we introduce the Navigation Graph, which models viewing behaviors in the temporal (segments) and the spatial (tiles) domains to perform the rate adaptation of tiled media associated with the view prediction. The Navigation Graph allows clients to perform view prediction more easily by sharing the viewing model in the same way in which media description information is shared in DASH. It is also useful for encoding the trajectory information in the media description file, which could also allow for more efficient navigation of 360-degree videos. This paper provides information about the creation of the Navigation Graph and its uses. The performance evaluation shows that the Navigation Graph based view prediction and rate adaptation outperform other existing tiled media streaming solutions. Navigation Graph is not limited to 360-degree video streaming applications, but it can also be applied to other tiled media streaming systems, such as volumetric media streaming for augmented reality applications.

360 video streaming is gaining popularity because of the new type of experience it creates. Tile-based approaches have been widely used in VoD 360 video streaming to save the network bandwidth. However, they cannot be extended to the case of live streaming because they assume the 360 videos stitched offline before streaming. Instead, stitching has to be done in real-time in live 360 video streaming. More importantly, the stitching speed as shown in our experiments is one order of magnitude lower than the network transmission speed, making stitching more of a deciding factor of the overall frame rate than the network transmission speed. In this paper, we design a stitching algorithm for tile-based live 360 video streaming that adapts stitching quality to make the best use of the timing budget. There are two main challenges. First, existing tile-based approaches do not consider various semantic information in different scenarios. Second, the decision of tiling schemes for tile-based stitching is non-trivial. To solve the above two challenges, we present an event-driven stitching algorithm for tile-based 360 video live streaming, which consists of such an event-driven model to abstract various semantic information as events and a tile actuator to make tiling scheme decisions. We implement a streaming system based on event-driven stitching called LiveTexture. To evaluate the proposed algorithm, we compare LiveTexture with other baseline systems and show that LiveTexture adapts well to various timing budgets by meeting 89.4% of the timing constraints. We also demonstrate that LiveTexture utilizes the timing budget more efficiently than others.

Indoor localization based on Wi-Fi fingerprints has been an active research topic for years. However, existing approaches do not consider the instability of access points (APs) which may be unreliable in practice, particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the unreliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the well-deployed APs (e.g., deployed by facility teams). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multi-mode antennas, and build a new indoor localization system called MMLOC to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs. A clustering based localization strategy is designed to enable a mobile terminal to figure out the RSSI (Received Signal Strength Indicator) for different antenna modes without requiring any synchronization. We have implemented a prototype system using smart APs and commercial smartphones. Experimental results demonstrate that MMLOC can reduce the number of necessary APs by 50%, and achieve the same or even better localization accuracy.

In recent years, virtual reality and augmented reality applications have seen a significant increase in popularity. This is due to multiple technology trends. First, the availability of new tethered and wireless head-mounted displays allows viewers to consume new types of content. Second, 360° omnidirectional cameras, in combination with production software, make it easier to produce personalized 360° videos. Third, beyond these new developments for creating and consuming such content, video sharing websites and social media platforms enable users to publish and view 360° video content. In this paper, we present challenges of 360° video streaming systems, give an overview of existing approaches for 360° video streaming, and outline research opportunities enabled by 360° video. We focus on the data model for 360° video and the different challenges and approaches of creating, distributing, and presenting 360° video content, including 360° video recording, storage, distribution, edge delivery, and quality-of-experience evaluation. In addition, we identify major research opportunities with respect to efficient storage, timely distribution, and cybersickness-free personalized viewing of 360° videos.

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers' quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers' noisy or even conflicting sensory data, truth discovery algorithms, which jointly estimate workers' data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers' strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism, named Theseus, that deals with workers' such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers' data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility. We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.

Internet of Things (IoT) applications generate massive amounts of real-time streaming data. IoT data owners strive to make predictions/inferences from these large streams of data often through applying machine learning, and image processing operations. A typical deployment of such applications includes edge devices to provide processing/storage operations closer to the location where the streaming data is captured. An important challenge for IoT applications is deciding which operations to execute on an edge device and which operations should be carried out in the cloud. In this paper, we propose a scalable dynamic programming algorithm called DROPLET, to partition operations in IoT applications across shared edge and cloud resources, while minimizing completion time of the end-to-end operations. We evaluate DROPLET using three real-world applications. Our results show that DROPLET finds a partitioning of operations having overall completion time within 4% of the optimum for these applications. It also scales to thousands of operations and outperforms closest heuristics in the literature, by being 10 times faster in running time while finding partitioning of operations with total completion time that is 20% better for the large applications that we simulated. We analyze DROPLET to show that it scales with total number of operations in log-linear time.

Recent years have witnessed the success of mobile crowd sensing systems, which outsource sensory data collection to the public crowd equipped with various mobile devices in a wide spectrum of civilian applications. We envision that crowd sensing could as well be very useful in a whole host of mission-driven scenarios, such as peacekeeping operations, non-combatant evacuations, and humanitarian missions. However, the power of crowd sensing could not be fully unleashed in mission-driven crowd sensing (MiCS) systems, unless workers are effectively incentivized to participate. Therefore, in this paper, taking into consideration workers' diverse quality of information (QoI), we propose Squadron, a quality-aware incentive mechanism for MiCS systems. Squadron adopts the reverse auction framework. It approximately minimizes the platform's total payment for worker recruiting in a computationally efficient manner, and recruits workers who potentially could provide high quality data. Furthermore, it also satisfies the desirable properties of truth-fulness and individual rationality. Through rigorous theoretical analysis, as well as extensive simulations, we validate the various aforementioned desirable properties held by Squadron.

An upcoming frontier for distributed computing might literally save lives in future military operations. In civilian scenarios, significant efficiencies were gained from interconnecting devices into networked services and applications that automate much of everyday life from smart homes to intelligent transportation. The ecosystem of such applications and services is collectively called the Internet of Things (IoT). Can similar benefits be gained in a military context by developing an IoT for the battlefield? This paper describes unique challenges in such a context as well as potential risks, mitigation strategies, and benefits.

Commercial use of Unmanned Aerial Vehicles (UAVs), or drones, promises to revolutionize the way in which consumers interact with retail services. However, the further adoption of UAVs has been significantly impeded by an overwhelming public outcry over the privacy implications of drone technology. While lawmakers have attempted to establish standards for drone use (e.g., No-Fly-Zones (NFZs)), at present a general technical mechanism for policy enforcement eludes state-of-the-art drones. In this work, we propose that Proof-of-Alibi (PoA) protocols should serve as the basis for enforcing drone privacy compliance. We design and implement AliDrone, a trustworthy PoA protocol that enables individual drones to prove their compliance with NFZs to a third party Auditor. AliDrone leverages trusted hardware to produce cryptographically-signed GPS readings within a secure enclave, preventing malicious drone operators from being able to forge geo-location information. AliDrone features an adaptive sampling algorithm that reacts to NFZ proximity in order to minimize the processing cost. Through laboratory benchmarks and field studies, we demonstrate that AliDrone provides strong assurance of geo-location while imposing an average of 1.5% overhead on CPU utilization and 0.3% of memory consumption. AliDrone thus enables the further proliferation of drone technology through the introduction of a trustworthy and accountable compliance mechanism.

Head movement prediction is the key enabler for the emerging 360-degree videos since it can enhance both streaming and rendering efficiency. To achieve accurate head movement prediction, it becomes imperative to understand user's visual attention on 360-degree videos under head-mounted display (HMD). Despite the rich history of saliency detection research, we observe that traditional models are designed for regular images/videos fixed at a single viewport and would introduce problems such as central bias and multi-object confusion when applied to the multi-viewport 360-degree videos switched by user interaction. To fill in this gap, this paper shifts the traditional single-viewport saliency models that have been extensively studied for decades to a fresh panoramic saliency detection specifically tailored for 360-degree videos, and thus maximally enhances the head movement prediction performance. The proposed head movement prediction framework is empowered by a newly created dataset for 360-degree video saliency, a panoramic saliency detection model and an integration of saliency and head tracking history for the ultimate head movement prediction. Experimental results demonstrate the measurable gain of both the proposed panoramic saliency detection and head movement prediction over traditional models for regular images/videos.

Nowadays the camera has developed into an indispensable and ubiquitous part of our life. It ensures the safety of people and their belongings, keeps records of special moments, or logs daily life. However, the ever-increasing amount of cameras surrounding us raised privacy concerns among people, who find themselves easily captured by a camera without themselves acknowledging it. To make matters worse, cameras, especially those on smart phones, are now more pervasive than ever before and can hardly be regulated as the recorders have full control of their cameras. Motivated by the privacy challenges originated from the ever-increasing and wide-spreading cameras, this paper presents the Real-time, Secure, and Privacy-aware Video Redaction System (ReSPonSe), which aims at protecting private information in personal videos according to permissions of people-in-video for other viewers to view them in the video. This system innovatively separates the production of videos into two stages: Encapsulation and Decapsulation. The first stage produces neutral videos in real-time while the second stage provides privacy-aware video to the viewer revealing private content of people-in-video who grants access rights to that viewer. The evaluation demonstrates the capability of this system to protect private information in videos with high efficiency and accuracy.

Currently, the surveillance camera-based person re-identification is still challenging because of diverse factors such as people’s changing poses and various illumination. The various poses make it hard to conduct feature matching across images, and the illumination changes make color-based features unreliable. In this article, we present SKEPRID, 1 a skeleton-based person re-identification method that handles strong pose and illumination changes jointly. To reduce the impacts of pose changes on re-identification, we estimate the joints’ positions of a person based on the deep learning technique and thus make it possible to extract features on specific body parts with high accuracy. Based on the skeleton information, we design a set of local color comparison-based cloth-type features, which are resistant to various lighting conditions. Moreover, to better evaluate SKEPRID, we build the PO8LI 2 dataset, which has large pose and illumination diversity. Our experimental results show that SKEPRID outperforms state-of-the-art approaches in the case of strong pose and illumination variation.

In this paper, we present a data acquisition and analysis framework for materials-to-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-to-devices digital data in a real-time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of novel services: a curation service for collecting data from microscopes and fabrication instruments, curating, and wrapping of data with extensive metadata in real-time and in a trusted manner, and a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our novel cloud framework can help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

Telehealth is a healthcare service that relies on exchanging information from one place to another to improve a patient's health status. In this demonstration, we aim to provide similar benefits to instantly bringing a doctor in the field to provide the right treatment at the right time for time-sensitive injuries. We present a telehealth system called Teleconsultant that enables near real-time communication between paramedics and doctors via videos captured from wearable cameras, this is crucial in the acute situations when the paramedic needs immediate assistance from the remote doctor that could help saving patients' lives. Teleconsultant includes capturing the video through body cameras worn by the paramedics, we refer to this video as wearable video. The video is transmitted over a heterogeneous wireless network to the remote doctor. Along the network path, video is analyzed in real-time to: (1) enhance video quality (e.g., video stabilization), and (2) detect time-sensitive injuries (e.g., stroke) so that remote doctors can be alerted and prepared when patient arrives via ambulance to the hospital. We demonstrate an end-to-end system to enable streaming of wearable video from the incident site to the hospital using body cameras worn by paramedics. Additionally, we demonstrate a framework for in-stream processing of the wearable video and we show two real-time video processing functions: stroke detection, and video stabilization.

In this paper we present a cyber-physiotherapy system (CyPhy) that brings daily rehabilitation to patient's home with supervision from trained therapist. CyPhy is able to capture and record RGB-D, skeleton, and physiotherapy-related medical sensing data streams from patient's exercises using multiple cameras and body sensors. With hours of exercises from every patient, that are captured every day from multiple cameras, therapists spend huge amount of their time watching videos to monitor the correctness of patients' moves. This becomes even more challenging in the presence of multiple cameras where the therapist might not know which camera stream shows the incorrect motion. In this paper, we explore the multicamera summarization problem from various aspects: (1) We first explore the types of exercises that benefit the most from using multiple cameras; (2) We propose a method to detect incorrect motion from multiple cameras in rehabilitation exercises; (3) We show how the analysis of incorrect motion is used to summarize the video and recommend the camera view that best visualizes the mistake. Our method for detecting incorrect motion achieves more than 92% accuracy at wide range of thresholds with significant improvement of 20% over single camera and 10% over the closest approach that uses multiple cameras.

The current state-of-the-art tele-medicine applications only allow audiovisual communication between a doctor and the patient, necessitating a clinician to physically examine the patient. The doctor relies on the physical examination performed by the clinician, along with the audiovisual dialogue with the patient. In this paper, a Haptic-enabled Tele-Immersive Musculoskeletal Examination (H-TIME) system is introduced, that allows doctors to physically examine musculoskeletal conditions of the patients remotely, by looking at the 3D reconstructed model of the patient in the virtual world, and physically feeling the patient's range of mobility using a haptic device. The proposed bidirectional haptic rendering in H-TIME can allow the doctor to evaluate a patient who suffers from problems in their upper extremities, such as the shoulder, elbow, wrist, etc., and evaluate them remotely. Real world user study was performed, between the doctors and the patients, and it highlighted the potential of the proposed system. The study indicated a high degree of correlation between the in-person and H-TIME evaluations of the patient. Both the doctors and patients involved in the study, felt that the system could potentially replace in-person consultations, someday.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers' noisy or even conflicting sensory data, truth discovery algorithms, which jointly estimate workers' data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers' strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism, named Theseus, that deals with workers' such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers' data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility. We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.

As more and more information systems are moving to the cloud, there have been efforts to deploy publish-subscribe (or pub/sub) systems in the cloud environment to take advantage of the elasticity of resources. As a result, there is a need to perform resource management for the cloud-based pub/sub systems that support various types of jobs, each consists of a series of tasks, or a workflow. Designing an efficient and effective resource management approach for the cloud-based pub/sub system is challenging because such an approach needs to be able to support flexible provisioning strategies, model the complex interactions between heterogeneous types of jobs, and provide dynamic resource allocation capability. In this paper, we formulate the resource management problem of elastic pub/sub system as optimization problems using different objectives functions. We model the elastic pub/sub system as a multiple-class open queuing network to derive system performance measures, and propose greedy algorithms to efficiently solve the optimization problems. Our evaluation based on simulation on real system show that our proposed solution outperforms the baseline and is robust in dealing with high-volume and fast-changing workload.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

Recent years have witnessed the proliferation of mobile crowd sensing (MCS) systems that leverage the public crowd equipped with various mobile devices (e.g., smartphones, smartglasses, smartwatches) for large scale sensing tasks. Because of the importance of incentivizing worker participation in such MCS systems, several auction-based incentive mechanisms have been proposed in past literature. However, these mechanisms fail to consider the preservation of workers' bid privacy. Therefore, different from prior work, we propose a differentially private incentive mechanism that preserves the privacy of each worker's bid against the other honest-but-curious workers. The motivation of this design comes from the concern that a worker's bid usually contains her private information that should not be disclosed. We design our incentive mechanism based on the single-minded reverse combinatorial auction. Specifically, we design a differentially private, approximately truthful, individual rational, and computationally efficient mechanism that approximately minimizes the platform's total payment with a guaranteed approximation ratio. The advantageous properties of the proposed mechanism are justified through not only rigorous theoretical analysis but also extensive simulations.

Free-viewpoint television (FTV) is a visionary application that provides immersive experience to the audience with the freedom of changing viewpoint during the video playout. However, live broadcasting and user interaction do not coexist in existing FTV systems. In this paper, we propose F.Live, a framework of FTV content dissemination that supports user-initiated viewpoint changing for live broadcasting. Simulation result of a large-scale experiment, based on camera array settings of existing Nagoya systems and EyeVision System, shows that F.Live is capable of supporting 100,000 concurrent audiences with free-viewpoint low user interaction latency and feasible bandwidth requirements.

This paper presents Physical Representation Description (PRD), a method for representing the spatial relationship between sources of multimedia streams in physical environments. PRD is an extension for the most widely used video streaming standard DASH. DASH supports the delivery of multiple synchronized streams (e.g., video, audio and sensor data) from a server to multiple clients, however DASH does not provide information about how the multi-modal sensory sources were actually located relative to each other when the data was captured. PRD extends DASH streaming standard by describing how the sources of multi-modal sensory streams are located relative to each other in the physical environment. This enables DASH clients to understand how the scene was captured in a physical space, and use this information to (1) differentiate and prioritize sensory streams according to their physical location, (2) enhance viewing experience, and (3) assist in easier spatial mapping from physical to virtual cyber space and vice versa. In this paper we describe the need for PRD in an example application called CyPhy, representing a multi-modal sensory cyberphysical environment for tele-physiotherapy in patients' homes. We then describe PRD and show how it can improve DASH streaming standard in delivering enhanced viewing experience under different network, and viewing conditions using physical spatial information of sensory sources.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from past literature, we capture such interactive effect, and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data, and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis, as well as extensive simulations.

Process mining is the task of extracting information from event logs, such as ones generated from workflow management or enterprise resource planning systems, in order to discover models of the underlying processes, organizations, and products. As the event logs often contain a variety of process executions, the discovered models can be complex and difficult to comprehend. Trace clustering helps solve this problem by splitting the event logs into smaller subsets and applying process discovery algorithms on each subset, resulting in per-subset discovered processes that are less complex and more accurate. However, the state-of-the-art clustering techniques are limited: the similarity measures are not process-aware and they do not scale well to high-dimensional event logs. In this paper, we propose a conceptualization of process's event logs as a heterogeneous information network, in order to capture the rich semantic meaning, and thereby derive better process-specific features. In addition, we propose SeqPathSim, a meta path-based similarity measure that considers node sequences in the heterogeneous graph and results in better clustering. We also introduce a new dimension reduction method that combines event similarity with regularization by process model structure to deal with event logs of high dimensionality. The experimental results show that our proposed approach outperforms state-of-the-art trace clustering approaches in both accuracy and structural complexity metrics.

The raising concerns of energy consumption and air pollution advance the development of electric vehicle technologies and promote the increased deployment of Electric Vehicles (EVs) towards electric transportation. The increasing number of EVs on the road network leads to a growing challenge of electricity management for the power grid to promptly supply electricity to EVs. In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this paper, we focus on studying the placement of energy sources and their charging facilities for EVs by developing a 2-stage planning process. We study the placement of two types of charging facilities: charging stations and dynamic wireless charging pads. The first stage of the planning process is to determine the optimal locations for placing the charging facilities to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage is to determine the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of the 2-stage planning process on a sample road network during our performance evaluation.

User-generated content, such as short video snippets or tweets, is increasingly used in event coverage even by professional media outlets. Especially in unforeseen events, or when dealing with large crowds, these snippets provide unique perspectives on the scene. While uploading a tweet does not impose much load on the communication system, uploading live video at today's camera resolutions consumes a significant amount of resources. At the same time, only a fraction of the uploaded streams is suitable for event coverage (e.g., shakiness of the video, focus on the scene, obstructions). By identifying the set of relevant streams early, and postponing the upload of other content, the available network resources can be dedicated to the upload of the most relevant streams. In this paper, we propose a set of strategies to collaboratively upload the most relevant streams at high quality by utilizing freed resources. We argue that these strategies can be exchanged during runtime to adapt to user dynamics and network heterogeneity, and present initial findings on the performance of our system.

In-motion inductive charging, or dynamic charging, is an emerging technology that allows electric vehicles (EVs) to be charged while on the move. Accurate billing for dynamic EV charging requires secure communication between the EVs and the utility, and could potentially require the secure delivery of small messages from the EVs to the utility at a very high rate, which is infeasible with the currently available solutions. In this paper we propose Fast Authentication for Dynamic EV Charging (FADEC) designed to meet the communication needs of in-motion inductive EV charging. FADEC features fast signing and verification, low communication overhead, and fast hand-off authentication to support EV mobility. Our simulations show that compared with ECDSA mandated by 802.11p standard, FADEC reduces data delivery delay by up to 97%, increases the data delivery ratio by more than an order of magnitude and enables timely data delivery even in a resource constrained environment.

3D video is a new form of video featuring omni-view and immersive properties that are beneficial to many applications like telediagnosis, remote physiotherapy and e-learning. This paper presents OmniViewer, a multi-modal 3D streaming system based DASH, enhancing the versatility of the existing DASH standard and delivery infrastructure. Besides 3D video, OmniViewer supports multi-modal media in the DASH content for real-time media-synchronized interactive services. OmniViewer provides a complete multi-modal 3D video streaming solution from recording to rendering through DASH, which fills the gap between monoscopic 3D systems and 3D streaming systems.

In this paper, we study the physical crowd-sensing problem and draw the connection to the vertex cover problem in graph theory. Since finding the optimal solution for minimum vertex cover problem is NP-complete and the well-known approximation algorithms do not perform well with under crowd-sensing scenario, we propose the notions of node observability and coverage utility score and design a new context-aware approximation algorithm to find vertex cover that is tailored for crowd-sensing task. In addition, we design human-centric bootstrapping strategies to make initial assignment of sensing devices in the physical crowd based on social information about the users (e.g., Interests, friendship). Our experiments on real-world data traces show that the proposed approach significantly outperforms the baseline approximation algorithms in terms of sensing coverage.

This paper presents OmniViewer, a multi-modal 3D video streaming system based on Dynamic Adaptive Streaming over HTTP (DASH) standard. OmniViewer allows users to view arbitrary side of a performer by choosing the view angle from 0° to 360°. Besides, according to the current available bandwidth, it can also adaptively change the bitrate of rendered 3D video for both smooth and high-quality view rendering. Finally, OmniViewer extends traditional DASH implementation to support multi-modal data streaming besides video and audio.

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the single-minded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-to-optimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.

3DTI Amphitheater is a live broadcasting system for dissemination of 3DTI (3D Tele-immersive) content. The virtual environment constructed by the system mimics an amphitheater in the real world, where performers interact with each other in the central circular stage, and the audience is placed in virtual seats that surround the stage. Users of the Amphitheater can be geographically dispersed and the streams created by the performer sites are disseminated in a P2P network among the participants. To deal with the high bandwidth demand and strict latency bound of the service, we identify the hierarchical priority of streams in construction of the content dissemination forest. Result shows that the Amphitheater outperforms prior 3DTI systems by boosting the application QoS by a factor of 2.8 while sustaining the same hundred-scale audience group.

With the fast growth of (online) content and the need for high quality content services, cloud data centers are increasingly becoming the preferred places to store data and retrieve it from. With a highly variable network traffic and limited resources, efficient server selection and data transfer rate allocation mechanisms become necessary. However, current approaches rely on random server selection schemes and inefficient data transmission rate control mechanisms. In this paper we present SCDA, an efficient server selection, resource allocation and enforcement mechanism with many salient features. SCDA has prioritized rate allocation mechanism to satisfy different service level agreements (SLA)s on throughput and delays. The allocation scheme can achieve max/min fairness. SCDA has a mechanism to detect and hence mitigate SLA violation in realtime. We have implemented SCDA in the NS2 simulator. Extensive experimental results confirm some of the design goals of SCDA to obtain a lower content transfer time and a higher throughput. The design of SCDA can achieve a content transfer time which is about 50% lower than the existing schemes and a throughput which is higher than existing approaches by upto than 60%.

The Internet of Things (IoT) concept has been around for some time and applications such as transportation, health-care, education, travel, smart grid, retail, are and will be major benefactors of this concept. However, only recently, due to technological advances in sensor devices and rich wireless connectivity, Internet of Things at scale is becoming reality. For example, Cisco's Internet of Things Group predicts over 50 billion connected sensory devices by 2020.

At the tipping of a paradigm shift in the way energy is produced, transmitted and delivered, the research efforts have not been paralleled by the curricular development. With rapid pace of changes in the field of Smart Grid (SG), the traditional research and educational efforts have been a major domain for electrical engineers. As a mode of discovery and education, interdisciplinarity facilitates broadened perspectives, ability to synthesize, analyze, integrate, and apply knowledge, and out-of-the-box thinking. The major contributions of this paper are discussion of requirements of the educational efforts in SG with special emphasis on multidisciplinarity, survey of the related work, which is the first in the literature to the best of our knowledge, and a discussion of the content for such an effort. A mixed team of power engineers and computer scientists are developing a layered curriculum starting from the introductory material from a variety of SG topics. A distinguishing advantage is the availability of many software tools and the state-of-the-art testbed as a result of years of research.

The energy sector worldwide has embarked on a transformational process to modernize the over-a-century-old power grid under an umbrella term of the Smart Grid. This vast infrastructural upgrade and operational change involves integration of a variety of advanced digital computing, communications and industrial control technologies. This brings new capabilities, but also necessitates a re-education of the aging workforce and training of the emerging workforce. While training does exist, the training approach and the accessibility of that training is often at odds with the needs of the utilities. To support this education and training need, in this paper we introduce the beginning of a modular, hands-on and open Smart Grid cybersecurity educational training platform and supporting materials together with an assessment of a preliminary version leveraged at the Trustworthy Cyber Infrastructure for the Power Grid (TCIPG) Summer School held in 2013. We base pedagogical pillars onto: (1) Active Learning that promotes analysis, synthesis, and evaluation of the content from Bloom's taxonomy, (2) The theory of project-based learning, (3) Piaget's learn-by-doing posture, and (4) Constructivist perspective of education. The main goal of this effort is to develop a complete, phased, and modular learning platform to provide the essential base knowledge and hands-on training exercises for understanding and demonstrating competency in Smart Grid cybersecurity.

In mission-based mobile environments such as airplane maintenance, workflow-based mobile sensor networks emerge, where mobile users (MUs) with sensing devices visit sequences of mission-driven locations defined by workflows, and demand the gathering of sensory data within mission durations. To satisfy this demand in a cost-efficient manner, mobile access point (AP) deployment needs to be part of the overall solution. Therefore, we study the mobile AP deployment in workflow-based mobile sensor networks. We categorize MUs' workflows according to a priori knowledge of MUs' staying durations at mission locations into complete and incomplete information workflows. In both categories, we formulate the cost-minimizing mobile AP deployment problem into multiple (mixed) integer optimization problems, satisfying MUs' QoS constraints. We prove that the formulated optimization problems are NP-hard and design approximation algorithms with guaranteed approximation ratios. We demonstrate using simulations that the AP deployment cost calculated using our algorithms is 50-60% less than the stationary baseline approach and fairly close to the optimal AP deployment cost. In addition, the run times of our approximation algorithms are only 10-25% of those of the branch-and-bound algorithm used to derive the optimal AP deployment cost.

3D Tele-Immersion (3DTI) environments are emerging as a new medium for human interactions and collaborations in the areas of education, sports training, physical medicine and rehabilitation. By adding a tactile element to a visually centered 3DTI environment, such applications can be made even more engaging. But it also opens up a few challenges in terms of fusing the visual and tactile data streams in a synchronous way. In this paper we describe a 3DTI Tele-Rehabilitation system with Microsoft Kinect cameras and hap tic devices. We describe some of the challenges we face in providing as well as quantifying a good quality of experience (QoE) in this system. We propose a set of solutions that: (i) improve the user's QoE (by using multi-modal prediction for handling latencies, better synchronization that accounts for the global state of the system, etc.), (ii) quantify the QoE (by designing a controlled virtual environment and by defining appropriate user QoE metrics for immersive tele-rehabilitation). The experimental results show a marked improvement in the performance of the system, consequently improving the user-experience. This is also verified by the results of the user performance study.

This paper proposes FreeViewer, a 3D Tele-Immersion view-control system that allows viewers to see arbitrary side of the performer by intelligently choosing the streams of a subset of cameras and changing the point of view in a 3D virtual space. The view changing is actuated by the change of the sensor data from wearable devices (eg. Google Glass, smartphone) on the performer able to monitor the current orientation.

Quality Provisioning concept has been with us for at least 25 years and it started with the claim to be one of the necessary building blocks for multimedia content distribution and delivery. A lot of research has been done on Quality of Service and Quality of Experience by the ACM Special Interest Group on Multimedia (SIGMM) community and other research communities. So where are we with respect to broader impact and deployment of Quality Provisioning in multimedia networks, systems and applications? Did it become a necessary building block for multimedia content delivery or not? During the talk I will go back and to the future, discussing my journey regarding quality topics ranging from Quality of Service (QoS) in multimedia networks and end systems, to Experiential Quality (QoE) for current and future multimedia applications. I will reflect on successes and failures of Quality Provisioning mechanisms, policies, algorithms, protocols and management frameworks in multimedia networks, systems and applications as they evolved from up to the point of an almost ubiquitous presence of multimedia services to the on-going discussions about network neutrality and multimedia service provisioning. I will also argue that the future for Quality Provisioning is bright with numerous exciting research problems since users expect at this point nothing but high quality multimedia content delivery anytime, anywhere, any content, and on any device.

In this paper we verify the insufficiency of Stevens' power law to describe the relationship between QoS and QoE factors. User studies that target different types of application scenarios of 3D Tele-immersion (3DTI) are conducted and the results show no significant power trend in the relationship between packet loss and perceptual quality metrics. We further verify that activity characteristics, activity objectives, and users' roles in the 3DTI session also have profound effects on the service quality aside to the QoS level. Thus, simple one-factor psychophysical laws are inadequate of serving as a QoS-QoE mapping model.

In this paper we present the 3DTI Amphitheater, a live broadcasting system for dissemination of 3DTI (3D Tele-immersive) content. The virtual environment constructed by the system mimics an amphitheater in the real world, where performers interact with each other in the central circular stage, and the audience is placed in virtual seats that surround the stage. Users of the Amphitheater can be geographically dispersed and the streams created by the performer sites are disseminated in a P2P network among the participants. To deal with the high bandwidth demand and strict latency bound of the service, we identify the hierarchical priority of streams in construction of the content dissemination forest. Result shows that the Amphitheater outperforms prior 3DTI systems by boosting the application QoS by a factor of 2.8 while sustaining the same hundred-scale audience group.

SaveAlert is an adaptive framework for crowd-monitoring and danger-detection using off-the-shelf smartphones and other peripherals such as smartwatches. It is a system that provides users with an increased awareness of their surroundings by detecting and notifying them of impending danger, by relying only on sensor data collected from the users. Our framework's novelty is in how it performs efficient sensor data collection from potentially a large number of people by limiting the disturbance and stress on the existing Wi-Fi and cellular infrastructure. To the best of our knowledge, this is the first crowd-monitoring framework that takes advantage of peer-to-peer connections to perform local aggregation to alleviate the stress on existing infrastructures for better scalability and efficiency.

Different 3D tele-immersive (3DTI) activities pose different prioritized requirements for application and network-level quality of service (QoS) to ensure a strong quality of experience (QoE) for participants. Some applications put heavy weight on audio quality, some consider higher quality for upper body video streams, and some seek very low end-to-end interactivity delay. In addition, a variation in streaming content may arise in the 3DTI space due to the participants' change in interests (e.g., view change). Therefore, there is a need for an adaptive multistream, multisite 3DTI session management strategy that is not only unobtrusive, but also optimizes prioritized QoS parameters in 3DTI content distribution based on user activity and content variation. To address this next generation session management problem, we revisit the design space of multistream and multisite 3DTI session layer. We present an evolutionary 3DTI session optimization approach using Open Session Management (OSM) architecture that uses a global view of participants and overlays network conditions to optimize QoS parameters. Experimental results with PlanetLab traces show that our optimization process is unobtrusive, and the optimized TI sessions provide higher satisfaction to the participants (in some cases up to 50% higher) compared to the current solutions in the 3DTI space.

Existing literature shows that Peer-to-Peer (P2P) content sharing can result in significant scalability gains in addition to assisting content distribution networks (CDNs). However, currently proposed CDN and P2P hybrid schemes do not provide accurate and efficient incentives to attract and maintain more peers. Besides, they do not use efficient prioritized congestion control and content source selection mechanisms to reduce content transfer time. We present Hincent, a quick content distribution protocol, which uses efficient prioritized rate allocation and content selection algorithms offering high incentives to participating peers. The fair incentives attract more peers which securely download and distribute contents. This in turn can benefit content providers and network operators. The Hincent rate allocations results in quicker content transfer time when compared with existing schemes. Hincent also employs effective rate enforcement mechanisms without requiring changes to the TCP/IP stack or to existing routers. Unlike existing centralized schemes such as YouTube, the design allows peers to have full control of (their) contents while sharing them with others using personal web servers. We have implemented Hincent in the NS2 simulator. Our detailed trace-based experiments show that Hincent outperforms existing schemes in terms of file download time and throughput by up to 30% on average. The results also demonstrate that Hincent obtains fair uplink prices for the uploaders and fair cost for the downloaders maintaining an overall system fairness. Besides, the results show the efficient Hincent enforcements of the prioritized allocations.

The design of Android is based on a set of unprotected shared resources, including those inherited from Linux (e.g., Linux public directories). However, the dramatic development in Android applications (app for short) makes available a large amount of public background information (e.g., social networks, public online services), which can potentially turn such originally harmless resource sharing into serious privacy breaches. In this paper, we report our work on this important yet understudied problem. We discovered three unexpected channels of information leaks on Android: per-app data-usage statistics, ARP information, and speaker status (on or off). By monitoring these channels, an app without any permission may acquire sensitive information such as smartphone user's identity, the disease condition she is interested in, her geo-locations and her driving route, from top-of-the-line Android apps. Furthermore, we show that using existing and new techniques, this zero-permission app can both determine when its target (a particular application) is running and send out collected data stealthily to a remote adversary. These findings call into question the soundness of the design assumptions on shared resources, and demand effective solutions. To this end, we present a mitigation mechanism for achieving a delicate balance between utility and privacy of such resources.

3D Teleimmersive (TI) Networks enable collaborative interactive activities in the same virtual space. The geographically distributed multi-site nature of TIs, the heterogeneity and multi-modality of their I/O devices and the diversity of activities in these environments pose new challenges in designing networked I/O device management for TIs not addressed by existing systems. In this paper we describe Decima, a holistic virtualized I/O Management. Decima enables dynamic seamless and universal access and management to asymmetric Teleimmersive activities using a large scale of heterogeneous TI devices. Our experiments show that Decima is lightweight in terms of CPU overhead and end-to-end delay and scales well as the number of devices in the TI network increases.

In smart grid, the scale of pole devices that monitor the health of power line is very large. Moreover, with the upgrade of smart grid, the number of these resource-constrained (in terms of memory and computation) devices is further increasing. These devices are easy targets to security attacks as they are accessible via wireless network, and use weak passwords for authentication and transferring telemetric data to the pole maintenance personnel. In this paper, we present a SCalable and Automated PAssword- CHanging protocol, SCAPACH, for unique authentication of human personnel (operator) and secure collection of telemetric data from a large number of pole devices. SCAPACH employs physical per- operator, per-pole-device information as well as changeable secret salts to generate new unique passwords and secret keys every time a pole device is accessed. Our experiments confirm that the password-changing protocol authenticates and transmits pole device data securely and in real-time under varying maintenance scenarios.

In view of the high resource demand of 3D Tele-immersion (3DTI), we propose a user activity-based resource adaptation scheme that adjusts the compression ratio of 3DTI videos according to the user activity. The compression technique we designed is based on the frame synthesis via feature-based morphing. This technique is customized for 3DTI video due to the special properties of its scenes and the depth information provided by the system. Via a machine learning approach, our system can classify the user's current activity and choose the most suitable priority among temporal resolution, spatial resolution, and resource consumption in the adaptation. Our results show that the resource saving can reach up to 25% without perceptible degradation of the 3DTI videos.

3D Tele-Immersion (3DTI) system brings 3D data of people from geographically distributed locations into the same virtual space to enable interaction in 3D space. One main obstacle of designing 3DTI system is to overcome its high bandwidth requirement when disseminating the 3D data over the network. Bandwidth adaptation framework is needed to handle the heterogeneity of the available network throughput. In this work, we propose an object-level bandwidth adaptation framework for 3DTI system (OBA3D). Unlike existing adaptation frameworks which work at the frame level or Group-of-picture (GOP) level, OBA3D takes advantage of the 3D semantics contained in the 3D frame. It conducts bandwidth adaptation at the object-level and achieves more fine-grained control over 3D video quality. We evaluate the performance of OBA3D on a real 3DTI testbed. OBA3D is shown to achieve good quality-of-experience when the network throughput is substantially lower than the required bandwidth for data transmission.

Software Defined Networking (SDN) has provided configurable access to remote network layer components from application hosts during application run-time. We have successfully enabled SDN switches to reduce network resource consumption and improve streaming latency in multi-party 3D tele-immersive (3DTI) applications. Instead of forwarding the same streams to multiple participants from the application host, local network switches replicate network packets towards multiple forwarding paths. However, due to the variable length of application frames generated from the 3DTI application, maintaining constant packet rates at the network switches cannot guarantee required application frame rates towards different forwarding paths. In this poster, we investigate the problem of guaranteeing application frame rates from the network layer switches. We formulate the problem and sketch a scalable solution using OpenFlow, which is a standard protocol developed for SDN.

Video conferencing applications pose fundamentally different service requirements than traditional data traffic on the Internet. Strong real-time interactivity is very important among participants unlike video streaming in VoD applications. Requirements are even more stringent in multi-stream and multi-site teleimmersive applications due to strong dependencies across geographically distributed streams. In this paper, we propose OpenSession, a cross-layer session-network control protocol for multi-stream multi-site 3D teleimmersion (3DTI) that improves interactivity, resource utilization and scalability. OpenSession decouples application layer data and control plane functionalities, and partially offloads the data plane functionalities to network layer switches. To control network layer switches during the session run-time, OpenSession leverages support from Software Defined Networking (e.g., OpenFlow). Through extensive evaluation with multi-stream 3D teleimmersion among four distributed sites and PlanetLab-based larger 3DTI setup, we show that OpenSession improves 3DTI interactivity and resource usage at the data plane. Furthermore, OpenSession keeps data plane robust against host failures and frequent route updates.

In view of the resource demanding nature of 3D Tele-immersion (3DTI), we apply Morphing-based Frame Synthesis (MBFS) on delivery of both online and offline 3DTI visual content to decrease the resource consumption without degrading the perceptual quality. We further investigate the relationship between the level-of-motion of the content and the effectiveness of MBFS. In light of the results, we propose an on-the-fly resource adaptor for 3DTI video transmission which utilizes a perceptual model built from data compiled by a series of subjective experiments. Results show that our adaptor achieves 43% to 87% compression ratio for offline compression on 3DTI videos of different baseline user activities, and a 10% on-the-fly bandwidth saving on complex user activity without perceptible degradation.

The usage and number of available video conferencing (VC) applications are rising as the high-bandwidth, low latency networks on which they depend become increasingly prevalent. Since VC applications support real-time human interaction, problems with performance that impair interactivity are social issues. Currently, performance measurements cannot easily be obtained due to the proprietary nature of VC applications, however, such measurements would be useful because they enable researchers to easily evaluate the performance impact of architectural and design decisions, quantitatively compare VC applications, and determine service level agreement (SLA) compliance. In this paper, we present a tool called Av Cloak that is capable of measuring several key performance metrics in proprietary VC applications: mouth-to-ear latency and jitter, capture-to-display latency and jitter, and audio-visual synchronization skew. AvCloak takes these measurements by wrapping ("cloaking") the VC application's audio/video inputs/outputs and transmitting timestamp data through them. At the sender side, AvCloak synthesizes media data encoding timestamps and feeds them to the VC application's media inputs, while at the receiver side, AvCloak decodes timestamps from the VC application's media outputs. Since AvCloak interacts with the target VC application only through its media inputs and outputs, it treats the target application as a black box and is thus applicable to arbitrary VC applications. We provide extensive analyses to measure AvCloak's overhead and show how to improve accuracy in measurements using two popular VC applications: Skype and Google+ Hangouts.

In view of the different demands on quality of service of different user activities in the 3D Tele-immersive (3DTI) environment, we combine activity recognition and real-time morphing-based compression and present the Activity-Aware Adaptive Compression. We implement this scheme on our 3DTI platform: the TEEVE Endpoint, which is a runtime engine to handle the creation, transmission and rendering of 3DTI data. User study as well as objective evaluation of the scheme show that it can achieve 25% more bandwidth saving compared to conventional 3D data compression as zlib without perceptible degradation in the user experience.

Being able to detect and recognize human activities is essential for 3D collaborative applications for efficient quality of service provisioning and device management. A broad range of research has been devoted to analyze media data to identify human activity, which requires the knowledge of data format, application-specific coding technique and computationally expensive image analysis. In this paper, we propose a human activity detection technique based on application generated metadata and related system metadata. Our approach does not depend on specific data format or coding technique. We evaluate our algorithm with different cyber-physical setups, and show that we can achieve very high accuracy (above 97%) by using a good learning model.

We present TEEVE Endpoint, which is a runtime engine to handle the creation, transmission and rendering of 3D Tele-immersive (3DTI) data and provides application programming interfaces (APIs) to developers to easily create 3DTI applications.

Different 3D tele-immersive (3DTI) activities pose different requirements for application and network level quality of service (QoS) to ensure a strong quality of experience (QoE) for participants. Some applications put heavy weight on audio quality, some consider higher quality for upper body video streams, and some seek very low end-to-end interactivity delay. In addition, a variation in streaming content may arise due to the participants' change of interests (e.g., view change). Therefore, there is a need for an adaptive multi-stream, multi-site 3DTI session management strategy, which is unobtrusive, and optimizes QoS parameters in the 3DTI content distribution based on the user activity and content variation. To address this next generation session management problem, we revisit the design space of multi-stream and multi-site 3DTI session layer. We propose an evolutionary 3DTI session optimization approach using an Open Session Management (OSM) architecture that uses a global view of participants and overlay network conditions to optimize prioritized QoS parameters. Experimental results with PlanetLab traces show that the optimization process is computationally unobtrusive, and the optimized TI sessions meet expectations of the participants up to 50% higher compared to the current solutions in the 3DTI space.

The evolution of multimedia applications has drastically changed human life and behaviors. New communication technologies lead to new requirements for multimedia synchronization. This article presents a historical view of temporal synchronization studies focusing on continuous multimedia. We demonstrate how the development of multimedia systems has created new challenges for synchronization technologies. We conclude with a new application-dependent, multilocation, multirequirement synchronization framework to address these new challenges.

introduction Introduction to the special section on the 20th anniversary of the ACM international conference on multimedia Authors: Klara Nahrstedt View Profile , Rainer Lienhart View Profile , Malcolm Slaney View Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 9Issue 1sOctober 2013 Article No.: 32pp 1–3https://doi.org/10.1145/2523001.2523003Published:17 October 2013Publication History 0citation112DownloadsMetricsTotal Citations0Total Downloads112Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper we analyze and study the impact of cloudlets in interactive mobile cloud applications. To study the impact we propose the design of cloudlet network and service architectures. Our study focuses on file editing, video streaming and collaborative chatting which are representative enterprise application scenarios. Initial simulation results show the performance gains of using cloudlets over using clouds in terms of data transfer delay and system throughput. When not more than two cloudlet wireless hops are used to transfer data, the cloudlet-based approach outperforms the cloud-based approach for all three application scenarios. With more cloudlet wireless hops under mobility, the cloud-based approach can give a better performance for some of the data transfers even though the cloudlet-based can outperform the cloud-based approach for most of the flows. In such scenarios, we suggest that an adaptive scheme should be used. For example, a scheme making an intelligent decision on either the cloudlet network or the cloud network, whichever gives minimum delay, can be used.

3D Tele-immersive systems create real-time multi-stream and multi-view 3D collaborative contents from multiple sites to allow interactive shared activities in virtual environments. Applications of 3DTI include online sports, tele-health, remote learning and collaborative arts. In addition to interactive participants in 3DTI environments, we envision a large number of passive non-interactive viewers that (a) watch the interactive activities in 3DTI shared environments, and (b) select views of the activities at run time. To achieve this vision, we present 4D Tele Cast, a novel multi-stream 3D content distribution framework for non-interactive viewers providing the functionality of multi-view selection. It addresses the following challenges: (1) supporting a large number of concurrent multi-stream viewers as well as multi-views, (2) preserving the unique nature of 3DTI multi-stream and multi-view dependencies at the viewers, and (3) allowing dynamic viewer behavior such as view changes and large-scale simultaneous viewer arrivals or departures. We divide the problem space into two: (1) multi-stream overlay construction problem that aims to minimize the cost of distribution of multi-stream contents, and maximize the number of concurrent viewers with sufficient viewer dynamism in terms of their resources and availabilities, and (2) effective resource utilization problem that aims to preserve the multi-stream dependencies in a view considering the heterogeneous resource constraints at the viewers. We evaluate 4D Tele Cast using extensive simulations with 3DTI activity data and Planet Lab traces.

Existing media playout scheduling (MPS) schemes usually focus on selecting and scheduling packets according to optimized Internet media metrics, which are only partially relevant to the subjective human perception in the interactive system. The MPS design challenges are two-fold. First, human preferences are concurrently dominated by multiple quality attributes of the streaming media whose perceptual tradeoffs were not well understood, so they were not used as an integral part of an efficient MPS design. Second, people's perceptions can be impacted by the flicker effect caused by Internet dynamics and the resulting MPS adaptations. In this paper, we propose a new and adaptive perception-based MPS scheme to deliver high-quality real-time interactive multimedia. We first investigate the perceptual tradeoffs among the multi-modal bundle streaming qualities in a real Internet environment. We then present our MPS design that finds the bundle quality tradeoffs, while minimizing flicker degradations. Evaluation results show the performance of our MPS scheme.

3D Tele-immersion (3DTI) system enables geographically distributed users to interact with each other in the virtual 3D space. Many 3DTI applications require users to have frequent physical movement in the application (e.g, 3D interactive exergaming, remote therapy). However, traditional user interaction (UI) solution (which includes large display, mouse/keyboard) for 3DTI system does not give users much freedom to move during the interaction and thus has difficulties to meet this requirement. In this work, we design and implement a novel UI solution TEEVE-Remote which utilizes state-of-the-art camera, mobile phone and display technologies to overcome the difficulties and therefore significantly improve the user experience of 3DTI system.

The ACM Special Interest Group on Multimedia (SIGMM) is celebrating the 20th anniversary of establishing its premier conference, the ACM International Conference on Multimedia (ACM Multimedia). The panel "Coulda, Woulda, Shoulda" is part of the celebration at the ACM Multimedia 2012. The panelists and the audience will discuss the 20 years of multimedia opportunities that our community has seen, took upon and pushed forward to advance the state of the art.

We present an immersive gaming demonstration using the minimum amount of wearable sensors. The game demonstrated is two-player tennis. We combine a virtual environment with real 3D representations of physical objects like the players and the tennis racquet (if available). The main objective of the game is to provide as real an experience of tennis as possible, while also being as less intrusive as possible. The game is played across a network, and this opens the possibility of two remote players playing a game together on a single virtual tennis pitch. The Microsoft Kinect sensors are used to obtain a 3D point cloud and a skeletal map representation of the player. This 3D point cloud is mapped on to the virtual tennis pitch. We also use a wireless wearable Attitude and Heading Reference System (AHRS) mote, which is strapped onto the wrist of the players. This mote gives us precise information about the movement (swing, rotation etc.) of the playing arm. This information along with the skeletal map is used to implement the physics of the game. Using this game we demonstrate our solutions for simultaneous data acquisition, 3D point-cloud mapping in a virtual space, use of the Kinect and AHRS sensors to calibrate real and virtual objects and for interaction of virtual objects with a 3D point cloud.

ITU-T G.1070 [7] is widely cited for evaluating the subjective quality of the video conferencing, but its findings cannot be applied to the tele-immersion. The reasons are two fold. First, a tele-immersive system offers end users an unmatched realistic and immersive experience by allowing them to collaborate in the joint virtual space. Second, the human activities in the shared space are not limited to the conferencing conversation. In this paper, we conduct a user study with 19 participants to investigate the human perceptions of two tele-immersive shared activities, where media samples of different qualities are evaluated using the comparative category rating method [9] in case of each activity. We compare our subjective results to those presented in G.1070, and demonstrate heterogeneous human perceptual impacts in different activities.

Explosion in Big Data has led to a surge in extremely large-scale Big Data analytics platforms, resulting in burgeoning energy costs. Big Data compute model mandates strong data-locality for computational performance, and moves computations to data. State-of-the-art cooling energy management techniques rely on thermal-aware computational job placement/migration and are inherently data-placement-agnostic in nature. T* takes a novel, data-centric approach to reduce cooling energy costs and to ensure thermal-reliability of the servers. T* is cognizant of the uneven thermal-profile and differences in thermal-reliability-driven load thresholds of the servers, and the differences in the computational jobs arrival rate, size, and evolution life spans of the Big Data placed in the cluster. Based on this knowledge, and coupled with its predictive file models and insights, T* does proactive, thermal-aware file placement, which implicitly results in thermal-aware job placement in the Big Data analytics compute model. Evaluation results with one-month long real-world Big Data analytics production traces from Yahoo! show up to 42% reduction in the cooling energy costs with T* courtesy of its lower and more uniform thermal-profile and 9x better performance than the state-of-the-art data-agnostic cooling techniques.

Mobile devices are gradually changing people's computing behaviors. However, due to the limitations of physical size and power consumption, they are not capable of delivering a 3D graphics rendering experience comparable to desktops. Many applications with intensive graphics rendering workloads are unable to run on mobile platforms directly. This issue can be addressed with the idea of remote rendering: the heavy 3D graphics rendering computation runs on a powerful server and the rendering results are transmitted to the mobile client for display. However, the simple remote rendering solution inevitably suffers from the large interaction latency caused by wireless networks, and is not acceptable for many applications that have very strict latency requirements. In this article, we present an advanced low-latency remote rendering system that assists mobile devices to render interactive 3D graphics in real-time. Our design takes advantage of an image based rendering technique: 3D image warping, to synthesize the mobile display from the depth images generated on the server. The research indicates that the system can successfully reduce the interaction latency while maintaining the high rendering quality by generating multiple depth images at the carefully selected viewpoints. We study the problem of viewpoint selection, propose a real-time reference viewpoint prediction algorithm, and evaluate the algorithm performance with real-device experiments.

This article presents a psychophysical study that measures the perceptual thresholds of a new factor called Color-plus-Depth Level-of-Details (CZLoD) peculiar to polygon-based 3D tele-immersive video. The results demonstrate the existence of Just Noticeable Degradation and Just Unacceptable Degradation thresholds on the factor. In light of the results, we design and implement a real-time perception-based quality adaptor for 3D tele-immersive video. Our experimental results show that the adaptation scheme can reduce resource usage (e.g., CPU cycles) while considerably enhancing the overall perceived visual quality. Our analysis confirms the potential temporal and spatial performance benefits achievable with CZLoD adaptation.

Reactive jamming nodes are the nodes of the network that get compromised and become the source of jamming attacks. They assume to know any shared secrets and protocols used in the networks. Thus, they can jam very effectively and are very stealthy. We propose a novel framework for identifying the reactive jamming nodes in wireless LAN (WLAN). We rely on the half-duplex nature of nodes: they cannot transmit and receive at the same time. Thus, if a compromised node jams a packet, it cannot guess the content of the jammed packet. More importantly, if an honest node receives a jammed packet, it can prove that it cannot be the one jamming the packet by showing the content of the packet. Such proofs of jammed packets are called "alibis" - the key concept of our approach. In this paper, we present an alibi framework to deal with reactive jamming nodes in WLAN. We propose a concept of alibi-safe topologies on which our proposed identification algorithms are proved to correctly identify the attackers. We further propose a realistic protocol to implement the identification algorithm. The protocol includes a BBC-based timing channel for information exchange under the jamming situation and a similarity hashing technique to reduce the storage and network overhead. The framework is evaluated in a realistic TOSSIM simulation where the simulation characteristics and parameters are based on real traces on our small-scale MICAz test-bed. The results show that in reasonable dense networks, the alibi framework can accurately identify both non-colluding and colluding reactive jamming nodes.

A new metric distortion over latency (DOL) is proposed in this paper to overcome the deficiency of the traditional metric interaction latency in measuring the interactive performance of the modern remote rendering systems, which are enhanced with different latency reduction techniques. The proposed metric is novel in combining both latency and rendering quality into one score for measurement. Our experiments validate that in many scenarios, our new metric can effectively distinguish the performance difference between systems while interaction latency can not. The paper also introduces how DOL can be efficiently calculated at runtime.

In this demonstration, we present two 3D tele-immersive games: light-saber dual and block fencing that merge 3D video representations of participants in real-time to enable remote interactions in a virtual world. The light-saber dual arranges participants in a symmetric setup where both participants interact with each other in a virtual world with similar goals. On the other hand, the block fencing creates an asymmetric setup where participants interact with virtual objects having different goals. Using these two setups, we address the challenges and novelty of our solutions in portable environment setup, data acquisition, multi-stream synchronization, multi-stream session management, mobile device rendering, and overlay communication in the design and implementation of advanced 3D tele-immersive systems.

"Networking of Multimedia Women" event is a continuation of an on-going conversation in the multimedia research community and efforts by the ACM SIGMM to engage and promote female researchers in multimedia community, enable networking of junior and senior female researchers, and give insights towards successful professional careers based on examples.

The emerging cloud gaming service has been growing rapidly, but not yet able to reach mobile customers due to many limitations, such as bandwidth and latency. We introduce a 3D image warping assisted real-time video coding method that can potentially meet all the requirements of mobile cloud gaming. The proposed video encoder selects a set of key frames in the video sequence, uses the 3D image warping algorithm to interpolate other non-key frames, and encodes the key frames and the residues frames with an H.264/AVC encoder. Our approach is novel in taking advantage of the run-time graphics rendering contexts (rendering viewpoint, pixel depth, camera motion, etc.) from the 3D game engine to enhance the performance of video encoding for the cloud gaming service. The experiments indicate that our proposed video encoder has the potential to beat the state-of-art x264 encoder in the scenario of real-time cloud gaming. For example, by implementing the proposed method in a 3D tank battle game, we experimentally show that more than 2 dB quality improvement is possible.

This paper presents a psychophysical study that measures the perceptual thresholds of a new factor called Color-plus-Depth Level-of-Detail peculiar to polygon-based 3D tele-immersive video. The results demonstrate the existence of Just Noticeable Degradation and Just Unacceptable Degradation thresholds on the factor. In light of the results, we describe the design and implementation of a real-time perception-based quality adaptor for 3D tele-immersive video. Our experimental results show that the adaptation scheme can reduce resource usage while considerably enhancing the overall perceived visual quality.

This paper presents a psychophysical approach to control a new factor called Color-plus-Depth Level-of-Detail in polygon-based 3D tele-immersive video. Based on our psychophysical study that demonstrates the existence of perceptual thresholds on the factor, we present a real-time perception-based quality adaptor for 3D tele-immersive video. Our experimental results show that the adaptation scheme can reduce resource usage while considerably enhancing the overall perceived visual quality.

3D tele-immersion (3DTI) has the potential of enabling virtual reality interaction among remote people with real-time 3D video. However, today's 3DTI systems still suffer from various performance issues, limiting their broader deployment, due to the enormous demand on temporal (computing) and spatial (networking) resources. Past research focused on system-centric approaches for technical optimization, without taking human users into the loop. We argue that human factors (including user preferences, semantics, limitations, etc.) are an important and integral part of the cyber-physical 3DTI systems, and should not be neglected. This thesis proposes a comprehensive, human-centric framework for managing video data and functions across the 3DTI pipeline. Our approach is comprehensive because it involves all components of the video function pipeline. It is also comprehensive in the sense that both temporal and spatial resource challenges are considered and tackled.

An ideal interactive 3D tele-immersion (3DTI) system is expected to disseminate and synchronize multi-streams with a shortest-possible latency among participating sites, achieve inter-stream synchronization, and bound both inter-sender and inter-receiver skews. This is, however, a key challenge because of (1) the coexistence of multi-modal, correlated, bandwidth-savvy streams from multiple source media, (2) the bounded bandwidth resources at each site, (3) the heterogeneous transmission end-to-end delays (EED) between sites and (4) the diversity of 3D views requested by multiple users. Our study of the existing content dissemination topologies reveals their inadequacy of handlings the complication and dynamics present in 3DTI systems.

Applications like Twitter which use chat-like short messaging systems (SMS) have been widely used in public, political, military, emergency, humanitarian and other fields. Such applications usually involve servers (controllers) which control and forward messages from a sending client to a receiving client. The use of mobile wireless networks for such messaging systems has been increasing at a fast pace. To cope with this increase, there need to be efficient communication protocols and algorithms. To design such protocols and algorithms requires extensive analysis and understanding of the behavior of the communicating nodes under a given mobility scenario. One of the good metrics to understand the performance of such protocols is the reliability of message delivery. In this paper we present analytical models of the average reliability of short (chat-like) message delivery in mobile wireless networks as a multivariate function of the transmission range, movement area dimensions, number of servers (base stations) and message deadline (lifetime) under moderate realistic assumptions which can be easily relaxed and extended. Simulation results show that our analytical models give very good estimation of the average reliability of message delivery

This paper presents a novel paradigm of Streaming as a Service (SAS) to model correlated multi-streaming in Distributed Interactive Multimedia Environments. We propose SAS Kernel, a generic, distributed, and modular service kernel realizing SAS paradigm. SAS Kernel features high exibility by employing a configurable interface to allow for input of correlated multi-streams (bundle of streams) from diverse types of sensory devices. It is also highly extensible by allowing user-controlled functions to be applied to bundle of streams in runtime. Experiments with real-world applications demonstrate that the SAS Kernel incurs low overhead in delay, CPU, and bandwidth demands.

It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper presents a novel framework named Jyotish, which constructs a predictive model by exploiting the regular pattern of people movement found in real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to exploit regularity of people movement and cluster Wifi access point information in Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in Bluetooth trace. Next, the Bluetooth trace with assigned locations is used to construct predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictors over real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.

In this paper1, we first characterize the fine-grained encounter pattern among mobile users found in a large-scale Bluetooth trace collected by 123 participants at University of Illinois campus from March to August 2010. Our characterization results show that the fine-grained encounter pattern is regular and predictable. We then design 3R routing protocol, which leverages the regularity of fine-grained encounter pattern among mobile nodes to maximize message delivery probability while preserving message delivery deadline. We evaluate and compare 3R with Prophet and Epidemic routing protocols over the collected trace. Evaluation results show that 3R outperforms other alternatives considerably by improving message delivery while reducing message overhead.

The growing popularity of 3-D movies has led to the rapid development of numerous affordable consumer 3-D displays. In contrast, the development of technology to generate 3-D content has lagged behind considerably. In spite of significant improvements to the quality of imaging devices, the accuracy of the algorithms that generate 3-D data, and the hardware available to render such data, the algorithms available to calibrate, reconstruct, and then visualize such data remain difficult to use, extremely noise sensitive, and unreasonably slow. In this paper, we present a multi-camera system that creates a highly accurate (on the order of a centimeter), 3-D reconstruction of an environment in real-time (under 30 ms) that allows for remote interaction between users. This paper focuses on addressing the aforementioned deficiencies by describing algorithms to calibrate, reconstruct, and render objects in the system. We demonstrate the accuracy and speed of our results on a variety of benchmarks and data collected from our own system.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this article, we present two privacy-preserving data aggregation schemes for additive aggregation functions, which can be extended to approximate MAX/MIN aggregation functions. The first scheme--- Cluster-based Private Data Aggregation (CPDA)---leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme--- Slice-Mix-AggRegaTe (SMART)---builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme (TAG), where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes.

We present a detailed evaluation and sensitivity analysis of an energy-conserving, highly scalable variant of the Hadoop Distributed File System (HDFS) called Green-HDFS. Green HDFS logically divides the servers in a Hadoop cluster into Hot and Cold Zones and relies on insightful data-classification driven energy-conserving data placement to realize guaranteed, substantially long periods(several days) of idleness in a significant subset of servers in the Cold Zone. Detailed lifespan analysis of the files in a large-scale production Hadoop cluster at Yahoo! points at the viability of Green HDFS. Simulation results with real-worldYahoo! HDFS traces show that Green HDFS can achieve 24% energy cost reduction by doing power management in only one top-level tenant directory in the cluster and meets all the scale-down mandates in spite of the unique scale-down challenges present in a Hadoop cluster. If Green HDFS technique is applied to all the Hadoop clusters at Yahoo! (amounting to 38000 servers), $2.1millioncan be saved in energy costs per annum. Sensitivity analysis shows that energy-conservation is minimally sensitive to the thresholds in Green HDFS. Lifespan analysis points out that one-size-fits-all energy-management policies won'tsuffice in a multi-tenant Hadoop Cluster.

We consider the problem of identifying insider-based attacks in the form of jammers in multi-channel wireless networks, where jammers have the inside knowledge of frequency hopping patterns and any protocols used in the wireless network. We propose a novel technique, called "alibi'', to identify the insider-based jammers in multi-channel wireless networks. Alibi is a form of defense whereby a defendant attempts to prove that he or she was elsewhere when the crime in question was committed. Starting from such simple concept, we develop an alibi framework to cope with insider-based jamming attackers in various situations including single/multiple jammer and lossy channels. We evaluate the framework according to several properties such as accuracy, detection time and network performance via TOSSIM simulation and analysis. The overall results of these protocols show a promising research direction to deal with insider-based jamming attacks.

The objective of this research is to present an energy-conserving, self-adaptive Commodity Green Cloud Storage, called Lightning. Lightning's File System dynamically configures the servers in the Cloud Storage into logical Hot and Cold Zones. Lightning uses data-classification driven data placement to realize guaranteed, substantially long, periods (several days) of idleness in a significant subset of servers designated as the Cold Zone, in the commodity datacenter backing the Cloud Storage. These servers are then transitioned to inactive power modes and the resulting energy savings substantially reduce the operating costs of the datacenter. Furthermore, the energy savings allow Lightning to improve the data access performance by incorporation of high-performance, though high-cost Solid State Drives (SSD) without exceeding the total cost of ownership (TCO) of the datacenter. Analytical cost model analysis of Lightning suggests savings in the upwards of $24 million in the TCO of a 20,000 server datacenter. The simulation results show that Lightning can achieve 46% energy costs reduction even when the datacenter is at 80% capacity utilization.

With the advent of video sharing websites (e.g. YouTube) and interactive 3D video systems (e.g. Teeve, Cave), multimedia based content delivery networks (CDN) have become ubiquitous. These CDN architectures comprise of several processing and disseminating entities (e.g. proxies and gateways) that require real-time processing and trans-coding of multimedia content that must be delivered before an established deadline. Furthermore, Virtual Machine Monitors (VMMs) emerged as a popular architecture for server consolidation and cost reduction in enterprise systems. CDN proxies and gateways would greatly benefit from the deployment of VMM architectures since VMMs allow virtualization of their resources for virtual private multimedia sessions in a cost-effective manner. However, current VMMs do not provide abstractions to efficiently map processes to CPU resources and they do not provide adequate Quality of Service (QoS) mechanisms for task prioritization and CPU management to guarantee the completion of multimedia sessions by their deadlines.

This paper presents Eureka: a generic methodology of measuring the instantaneous (per second) bandwidth usage of networked games and applications in run time. Eureka starts with constructing a priority queue and sending low priority traffic through it. Then, the application under study is started with its packets marked as high priority. These packets therefore displace the previously existing low priority packets on the priority queue. We can measure this displacement and thus, the bandwidth used by the application. Unlike tcpdump this can be done while the application is running and without knowing any application specific data. In this paper we use Eureka to study the bandwidth usage of Massive Multiplayer Online Games (MMOGs), BZFlag and World of Warcraft-Trial, along with the 3D multi-site collaborative environments. A major strength of our methodology is that it provides run-time estimations of the bandwidth usage without interfering with the application, the environment, or the server/peer data. Moreover, Eureka is independent of the application and new infrastructure or tools are not required.

Though the variety of desktop real time stereo vision systems has grown considerably in the past several years, few make any verifiable claims about the accuracy of the algorithms used to construct 3D data or describe how the data generated by such systems, which is large in size, can be effectively distributed. In this paper, we describe a system that creates an accurate (on the order of a centimeter), 3D reconstruction of an environment in real time (under 30 ms) that also allows for remote interaction between users. This paper addresses how to reconstruct, compress, and visualize the 3D environment. In contrast to most commercial desktop real time stereo vision systems our algorithm produces 3D meshes instead of dense point clouds, which we show allows for better quality visualizations. The chosen representation of the data also allows for high compression ratios for transfer to remote sites. We demonstrate the accuracy and speed of our results on a variety of benchmarks.

Demand for 3D visualization is increasing in mobile devices as users have come to expect more realistic immersive experiences. However, limited networking and computing resources on mobile devices remain challenges. A solution is to have a proxy-based framework that offloads the burden of rendering computation from mobile devices to more powerful servers. We present the implementation of a framework for parallel remote rendering using commodity Graphics Processing Units (GPUs) in the proxy servers. Experiments show that this framework substantially improves the performance of rendering computation of 3D video.

Network coding has been shown to be capable of greatly improving quality of service in P2P live streaming systems (e.g., IPTV). However, network coding is vulnerable to pollution attacks where malicious nodes inject into the network bogus data blocks that are combined with other legitimate blocks at downstream nodes, leading to incapability of decoding the original blocks and substantial degradation of network performance. In this paper, we propose a novel approach to limiting pollution attacks by rapidly identifying malicious nodes. Our scheme can fully satisfy the requirements of live streaming systems, and achieves much higher efficiency than previous schemes. Each node in our scheme only needs to perform several hash computations for an incoming block, incurring very small computational latency. The space overhead added to each block is only 20 bytes. The verification information given to each node is independent of the streaming content and thus does not need to be redistributed. The simulation results based on real PPLive channel overlays show that the process of identifying malicious nodes only takes a few seconds even in the presence of a large number of malicious nodes.

Distributed Interactive Multimedia Environments (DIMEs) enable geographically distributed people to interact with each other in a joint media-rich virtual environment for a wide range of activities, such as art performance, medical consultation, sport training, etc. The real-time collaboration is made possible by exchanging a set of multi-modal sensory streams over the network in real time. The characterization and evaluation of such multi-stream interactive environments is challenging because the traditional Quality of Service metrics (e.g., delay, jitter) are limited to a per stream basis. In this work, we present a novel cyber-physical, spatio-temporal QoS model that includes new metrics to measure and evaluate QoS for a bundle of streams and across bundles of streams. The new model allows better assessment of performance of any DIME application which employs multiple sensors. We validate our model by comparing the QoS performance of bundle of streams over three transport protocols - UDP, DCCP, and TCP in a 3D tele-immersive test bed.

Distributed Interactive Multimedia Environments (DIMEs) show important dependency constraints between application and underlying system components over time. For example, the video frame rate and the underlying bandwidth usage have a strong performance dependency. Performance dependencies must also be considered among distributed components. These dependencies over a time-span form correlation relationships. Violations of such correlation relationships represent collective anomalies. Users and most specifically DIME application developers face problems of finding (detecting), localizing such anomalies, and adapting against them in real-time. Current practices are to collect joint application-system metadata characterizing behaviors of application and system components while a DIME session is running, and then analyze them offline. Our goal is to provide a framework, called DIAMOND, that allows for real-time and unobtrusive collection and organization of joint application-system metadata in order to assist in finding such correlation violations in the system. DIAMOND works in four steps: (a) real-time metadata collection, (b) metadata processing to allow efficient computation of correlation constraints, (c) metadata distribution for efficient clustering of distributed metadata, and (d) anomaly detection, localization, and evolution monitoring based on violations of correlation relationships. Our results from real implementations and simulations with Planet Lab traces show the effectiveness of DIAMOND in terms of network overhead and anomaly detection time.

In this paper, we present the results from a quantitative and qualitative study of distributed gaming in 3D tele-immersive (3DTI) environments. We explore the Quality of Experience (QoE) of users in the new cyber-physical gaming environment. Guided by a theoretical QoE model, we conducted a case study and evaluated the impact of various Quality of Service (QoS) metrics (e.g., end-to-end delay, visual quality, etc.) on 3DTI gaming experience. We also identified a number of non-technical factors that are not captured by the original theoretical model, such as age, social interaction, and physical setup. Our analysis highlights new implications for the next-generation gaming system design, as well as a more comprehensive conceptual framework that captures non-technical influences for user experience in such environments.

As an emerging technology, 3D video has shown a great potential to become the next generation media for tele-immersion. However, streaming and rendering this dynamic 3D data in real-time requires tremendous network bandwidth and computing resources. In this paper, we build a remote rendering model to better study different remote rendering designs and define 3D video rendering as an optimization problem. Moreover, we design a 3D video remote rendering system that significantly reduces the delay while maintaining high rendering quality. We also propose a reference viewpoint prediction algorithm with super sampling support that requires much less computation resources but provides better performance than the search-based algorithms proposed in the related work.

Though the quality of imaging devices, the accuracy of algorithms that construct 3D data, and the hardware available to render such data have all improved, the algorithms available to calibrate, reconstruct, and then visualize such data are difficult to use, extremely noise sensitive, and unreasonably slow. In this paper, we describe a multi-camera system that creates a highly accurate (on the order of a centimeter), 3D reconstruction of an environment in real time (under 30 ms) that allows for remote interaction between users. The paper addresses the aforementioned deficiencies by featuring an overview of the technology and algorithms used to calibrate, reconstruct, and render objects in the system. The algorithm produces partial 3D meshes, instead of dense point clouds, which are combined on the renderer to create a unified model of the environment. The chosen representation of the data allows for high compression ratios for transfer to remote sites. We demonstrate the accuracy and speed of our results on a variety of benchmarks and data collected from our own system.

This paper1 presents a novel framework called UIM2, which collects both location information and ad hoc contacts of the human movement at the University of Illinois campus using Google Android phones. Each UIM experiment phone encompasses a Bluetooth scanner and a wifi scanner capturing both Bluetooth MAC addresses and wifi access point MAC addresses in proximity of the phone. Then, Bluetooth MAC addresses are used to infer contact information and the wifi MAC addresses are used to infer physical location of the phone. Using the contact and location information, we investigate first the sensitivity analysis on contact duration and inter-contact duration. Then, we characterize the regularity of people movement, visit duration of people at locations, and the popularity of locations. Finally, we present the Hybrid Epidemic data dissemination protocol, which uses both wifi access point and ad hoc contact to expedite the data forwarding. We evaluate Hybrid Epidemic protocol with our collected ad hoc and wifi traces and find that in comparison with Epidemic data dissemination protocol, the Hybrid Epidemic protocol improves data forwarding delay considerably.

Synchronization is a challenge in the multi-site 3D tele-immersion (3DTI) because it is complicated by the coexistence of highly correlated heterogenous streams from multiple sources, and the need for multi-stream resynchronization when user views change. To address the problems, we present TSync: a new multi-tier synchronization framework for 3DTI which can effectively reduce the multi-stream sync skews. Our contributions are focusing on (1) the use of timed synchronization points for multi-stream synchronization; (2) Internet bandwidth estimation based on machine learning; (3) the cooperative frame rate allocation for correlated multi-streams and (4) the resynchronization protocol used when user views change. Experimental results show that TSync can successfully achieve the synchronization of multi-source heterogeneous streams in 3DTI under Internet dynamics.

3D television, 3D movie experiences, 3D tele-immersive and tele-presence environments, and extensive 3D gaming environments from Nintendo and Microsoft are becoming integral parts of our lives. Further advances in stereo and multi-view video, 3D graphics, spatial audio, Wii-like interface, 3D displays, and other multi-modal sensory devices, all contributing to acquisition, generation, display and control of new 4D media, already have and will have major impact on the next generation of multimedia systems and networks.

WiFi localization and tracking of indoor moving objects is an important problem in many contexts of ubiquitous buildings, first responder environments, and others. Previous approaches in WiFi-based indoor localization and tracking either assume prior knowledge of indoor environment or assume many data samples from location-fixed WiFi sources (i.e. anchor points). However, such assumptions are not always true, especially in emergency scenarios. This paper explores the possibility of real-time indoor localization and tracking without any knowledge of indoor environment and with real-time data samples from only few anchor points outside the building. By using a small set of synchronized directional antennas as outdoor anchor points to actively scan in various directions, a moving device inside the building can be localized and tracked from the received signal in real-time manner. The paper proposes an angle-of-arrival estimator for accurate localization and adaptive per-antenna angular scheduling for real-time indoor tracking. The validation results from real experiment and simulation yield effectiveness and accuracy of the proposed schemes.

With emerging demand for online surveillance and management, persistent and reliable connectivity to base stations (BSs) is indispensable so that network operators, which have access to all BSs, can collect measurements from all wireless terminals in the field, monitor their status and respond to critical incidents in real-time. However, disconnected networks make reliable BS connectivity problematic. Many papers have studied the problem of placing the minimum number of relays to retain connectivity. However, none of them considered polymorphous networks, which have multiple topologies, due to terminal movement, unsynchronized wakeup schedule and packet forwarding policy. Our paper makes the first attempt towards the study of relay placement problem in polymorphous networks. We propose two heuristic algorithms, topology stitch algorithm and topology iterative algorithm, which are built upon constrained relay placement algorithms for monomorphous networks with a single topology. Also, we propose the weigh-and-place algorithm (WPA), which optimizes relay placement across topologies with balanced load based on integer programming formulation. Evaluation shows that WPA places a smaller number of relays than the other two heuristic algorithms and achieves good load balance among multiple BSs.

The recent development of high-speed data transmission over wireless cellular networks has enabled the delivery of multimedia broadcasting services to mobile users. These services involve a range of interactions among different system components, including the wireless channel, the network, and mobile devices, making it crucial for the service provider to verify the model, design, and behavior of a new service before it is deployed. However, previous studies have largely relied on network simulations or scaled experiments, and there has been little work on the sort of unified framework for quality-of-service (QoS) assessment, which considers the interactions between components, that we propose in this paper. Accurate models of the wireless channel, the network, and the data processing that takes place on an embedded system of a mobile client, are integrated within our framework, and allow us to predict several key system metrics and the quality of the video stream as it is perceived by users. Furthermore, different models of system components can be easily plugged in to extend this framework. As an example application, we analyze the performance of the process of decoding scalable videos on ARM-based mobile embedded systems in CDMA2000 wireless cellular networks.

This article presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system, the most popular and largest p2p IPTV (Internet Protocol Television) system today. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of node degree, session length, and peer participation in simultaneous overlays. Our studies reveal characteristics of multimedia streaming p2p overlays that are markedly different from existing file-sharing p2p overlays. Specifically, we find that: (1) PPLive overlays are similar to random graphs in structure and thus more robust and resilient to the massive failure of nodes, (2) Average degree of a peer in the overlay is independent of the channel population size and the node degree distribution can be fitted by a piecewise function, (3) The availability correlation between PPLive peer pairs is bimodal, that is, some pairs have highly correlated availability, while others have no correlation, (4) Unlike p2p file-sharing peers, PPLive peers are impatient and session lengths (discretized, per channel) are typically geometrically distributed, (5) Channel population size is time-sensitive, self-repeated, event-dependent, and varies more than in p2p file-sharing networks, (6) Peering relationships are slightly locality-aware, and (7) Peer participation in simultaneous overlays follows a Zipf distribution. We believe that our findings can be used to understand current large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems.

Three-dimensional tele-immersive (3DTI) environments have great potential to promote collaborative work among geographically distributed users. However, most existing 3DTI systems only work with two sites due to the huge demand of resources and the lack of a simple yet powerful networking model to handle connectivity, scalability, and quality-of-service (QoS) guarantees. In this article, we explore the design space from the angle of multi-stream management to enable multi-party 3DTI communication. Multiple correlated 3D video streams are employed to provide a comprehensive representation of the physical scene in each 3DTI environment, and are rendered together to establish a common cyberspace among all participating 3DTI environments. The existence of multi-stream correlation provides the unique opportunity for new approaches in QoS provisioning. Previous work mostly concentrated on compression and adaptation techniques on the per-stream basis while ignoring the application layer semantics and the coordination required among streams. We propose an innovative and generalized ViewCast model to coordinate the multi-stream content dissemination over an overlay network. ViewCast leverages view semantics in 3D free-viewpoint video systems to fill the gap between high-level user interest and low-level stream management. In ViewCast, only the view information is specified by the user/application, while the underlying control dynamically performs stream differentiation, selection, coordination, and dissemination. We present the details of ViewCast and evaluate it through both simulation and 3DTI sessions among tele-immersive environments residing in different institutes across the Internet2. Our experimental results demonstrate the implementation feasibility and performance enhancement of ViewCast in supporting multi-party 3DTI collaboration.

Three-dimensional tele-immersive (3DTI) environments have great potential to promote collaborative work among geographically distributed users. However, most existing 3DTI systems only work with two sites due to the huge demand of resources and the lack of a simple yet powerful networking model to handle connectivity, scalability, and quality-of-service (QoS) guarantees.

Broadcast is a crucial yet expensive building block for many applications in bandwidth-scarce mobile wireless ad hoc networks. We propose a hybrid deterministic/probabilistic, decentralized broadcast protocol with adjustable broadcast reliability and overhead. The paper first proposes a purely probabilistic, topology-aware broadcast algorithm. The probabilistic broadcast adjusts each node's broadcast forwarding probability locally such that the average broadcast reliability requirement is met. An extension of the probabilistic broadcast to tolerate node mobility and packet loss is then presented. Furthermore, the paper augments the proposed probabilistic broadcast scheme with an existing deterministic broadcast protocol in order to reduce excessive broadcast overhead. The proposed hybrid protocol, called HybridCast, combines good characteristics of probabilistic broadcasts, such as adjustable reliability and resilience to mobility, with good characteristics of deterministic broadcasts, such as few retransmissions and low packet collisions. The simulation results show that the proposed protocol can achieve the system's reliability requirement with good tolerance to mobility and packet losses while incurring low broadcast overhead.

Finding the most likely path satisfying a requested additive Quality-of-Service (QoS) value, such as delay, when link metrics are defined as random variables by known probability distributions is NP-Hard. We transform the probability distributions into the Laplace domain, find the Laplace Transform of their convolutions and numerically inverse to find the distribution function in the time domain. Picard's iterative method of successive approximations is used to find the solution. To the best of our knowledge, ours is the first to propose a transform-based approach for the QoS routing problem of finding the most likely path. Simulations show that our stochastic approach (1) Selects correct paths more frequently, (2) Incurs less overhead with respect to the dissemination and processing of state information, and (3) Reduces the churn by selecting more stable paths.

Users and administrators of large distributed systems are frequently in need of monitoring and management of its various components, data items and resources. Though there exist several distributed query and aggregation systems, the clustered structure of tele-immersive interactive frameworks and their time-sensitive nature and application requirements represent a new class of systems which poses different challenges on this distributed search. Multi-attribute composite range queries are one of the key features in this class. Queries are given in high level descriptions and then transformed into multi-attribute composite range queries. Designing such a query engine with minimum traffic overhead, low service latency, and with static and dynamic nature of large datasets, is a challenging task. In this paper, we propose a general multi-attribute based range query framework, Q-Tree, that provides efficient support for this class of systems. In order to serve efficient queries, Q-Tree builds a single topology-aware tree overlay by connecting the participating nodes in a bottom-up approach, and assigns range intervals on each node in a hierarchical manner. We show the relative strength of Q-Tree by analytically comparing it against P-Tree, P-Ring, Skip-Graph and Chord. With fine-grained load balancing and overlay maintenance, our simulations with PlanetLab traces show that our approach can answer complex queries within a fraction of a second.

In WiFi-based content distribution community infrastructure (CDCI), file servers are deployed in diverse locations around cities, caching popular files interesting to a community. They serve file download requests from mobile users in proximity via WiFi. In this paper, we study the optimal caching strategy for file servers in CDCI so that the mean file retrieval probability within deadline is maximized, subject to storage capacity constraints of each file server. The optimal caching strategy depends on many factors, such as users' mobility patterns, access point topology, file popularity, etc. We have formalized this content management problem as a mixed integer optimization problem. Because of NPhardness and a large variable space of this optimization problem, we propose a heuristic algorithm MobaSsign to allocate file blocks. Extensive experiments show that our heuristic caching strategy considering mobility patterns improves the file retrieval probability within deadline.

The recent development of high-speed data transmission over wireless networks enables multimedia broadcasting service to mobile users. Multimedia broadcasting service involves interactions among different system and network components, so it is crucial for the service provider to verify the correctness of system/service model and design, and their behaviors before a new type of service is deployed. However, due to limitations of using network simulations or scaled experimental testbeds, there has been none of research on such verification and simulation framework in 3G broadcasting networks. Therefore, we propose a simulation and analysis framework for multimedia broadcasting service over wireless networks. With concrete modeling of wireless physical channel, network, and data processing on a client device, it enables the prediction of various interesting system parameters and perceived quality of multimedia streams to users. Different models of system and network components can be plugged easily in our simulation framework for further extensions. Using this framework, we analyze the processing performance for decoding scalable videos on mobile devices in CDMA2000 wireless networks.

The next-generation social communication medium, tele-immersion, is receiving increasing attention from both the research and industrial communities. It enables interaction between geographically distributed sites through realistic reconstruction of physical scenes in immersive spaces. While the previous research focused on real-world measurements of implemented tele-immersive systems, we take a model-based approach to study the system more generally and thoroughly. With a model validated with actual measurements, we evaluate the system performance in a larger scale, and understand the quantitative effect of the algorithmic enhancements that were proposed in the literature. We also predict futuristic performance characteristic and show that high interactivity can be foreseen with hardware advances in the near future.

A remote desktop utility system is an emerging client/server networked model for enterprise desktops. In this model, a shared pool of consolidated compute and storage servers host users' desktop applications and data respectively. End-users are allocated resources for a desktop session from the shared pool on-demand, and they interact with their applications over the network using remote display technologies. Understanding the detailed behavior of applications in these remote desktop utilities is crucial for more effective QoS management. However, there are challenges due to hard-to-predict workloads, complexity, and scale. In this paper, we present a detailed modeling of a remote desktop system through case study of an Office application - email. The characterization provides insights into workload and user model, the effect of remote display technology, and implications of shared infrastructure. We then apply these learnings and modeling results for improved QoS resource management decisions - achieving over 90% improvement compared to state of the art allocation mechanisms. We also present discussion on generalizing a methodology for a broader applicability of model-driven resource management.

It is challenging to provide authentication to time-critical multicast data, where low end-to-end delay is of crucial importance. Consequently, it requires not only efficient authentication algorithms to minimize computational cost, but also avoidance of buffering packets so that the data can be immediately processed once being presented. Desirable properties for a multicast authentication scheme also include small communication overhead, tolerance to packet loss, and resistance against malicious attacks. In this paper, we propose a novel signature model - Time Valid One-Time Signature (TV-OTS) - to boost the efficiency of regular one-time signature schemes. Based on the TV-OTS model, we design an efficient multicast authentication scheme "TV-HORS" to meet the above needs. TV-HORS combines one-way hash chains with TV-OTS to avoid frequent public key distribution. It provides fast signing/verification and buffering-free data processing, which make it one of the fastest multicast authentication schemes to date in terms of end-to-end computational latency (on the order of microseconds). In addition, TV-HORS has perfect tolerance to packet loss and strong robustness against malicious attacks. The communication overhead of TV-HORS is much smaller than regular OTS schemes, and even smaller than RSA signature. The only drawback of TV-HORS is a relatively large public key of size 8 KB to 10 KB, depending on parameters.

Broadcast communication prevails for data dissemination and resource discovery. In mission-critical applications, extensive information sharing and coordination endow broadcast with new features: a large number of active broadcast sources, probabilistic broadcast reception and high receiving rate. We identify this type of broadcast traffic as ASP Bcast traffic. Many efforts have been made to authenticate broadcast source and prevent content modification in a light-weighted way using one-way hash chain (TESLA). However, they do not scale to a large number of senders. In addition, authentication delay increases under packet losses and probabilistic broadcast. The longer authentication is postponed, the longer packets are buffered, which poses a memory-based denial-of-service (DoS) threat. In this paper, we will present an efficient authentication protocol for ASP Bcast traffic, called ChainFarm. We propose an algorithm for parameter configuration to satisfy both memory and delay requirements with maximal resilience against compromise. Simulation results agree with our analysis and show distinct performance improvement.

With the growing proliferation of conversational media and devices for generating multimedia content, the Internet has seen an expansion in websites catering to user-generated media. Most of the user-generated content is multimodal in nature as it has videos, audio, text (in the form of tags), comments and so on. Content analysis is a challenging problem on this type of media since it is noisy, unstructured and unreliable. In this paper we propose VideoMule, a consensus learning approach for multi-label video classification from noisy user-generated videos. In our scheme, we train classification and clustering algorithms on individual modes of information such as user comments, tags, video features and so on. We then combine the results of trained classifiers and clustering algorithms using a novel heuristic consensus learning algorithm which as a whole performs better than each individual learning model.

At the convergence of computer vision, graphics, and multimedia, the emerging 3D video technology promises immersive experiences in a truly seamless environment. However, the requirements of huge network bandwidth and computing resources make it still a big challenge to render 3D video on mobile devices at real-time. In this paper, we present how remote rendering framework can be used to solve the problem. The differences between dynamic 3D video and static graphic models are analyzed. A general proxy-based framework is presented to render 3D video streams on the proxy and transmit the rendered scene to mobile devices over a wireless network. An image-based approach is proposed to enhance 3D interactivity and reduce the interaction delay. Experiments prove that the remote rendering framework can be effectively used for quality 3D video rendering on mobile devices in real time.

The past decades have witnessed a rapid growth of Distributed Interactive Multimedia Environments (DIMEs). Despite their intensity of user-involved interaction, the existing evaluation frameworks remain very much system-centric. As a step toward the human-centric paradigm, we present a conceptual framework of Quality of Experience (QoE) in DIMEs, to model, measure, and understand user experience and its relationship with the traditional Quality of Service (QoS) metrics. A multi-displinary approach is taken to build up the framework based on the theoretical results from various fields including psychology, cognitive sciences, sociology, and information technology. We introduce a mapping methodology to quantify the correlations between QoS and QoE, and describe our controlled and uncontrolled studies as illustrating examples. The results present the first deep study to model the multi-facet QoE construct, map the QoS-QoE relationship, and capture the human-centric quality modalities in the context of DIMEs.

We present MobileTI, a portable tele-immersive system that merges 3D video representations of users in real time to enable remote collaboration across geographical distances. With portability as a main goal, we address the challenges in the camera setup, time synchronization, video acquisition, and networking in the design and implementation of the system. Having been deployed in public performances, MobileTI proves to be effective, efficient, and user-friendly. Our experimental findings in terms of technical performance and user feedback are presented.

Multi-stream/multi-site 3D video collaborative systems are promising as they enable remote users to interact in a 3D virtual space with a sense of co-presence. However, the decentralized content dissemination remains a challenge. In this work, we explore approaches to construct adaptive overlay based on the users' visual interest in the collaborative space. Particularly, we consider the practical challenge that the user's interest might change dynamically. We propose, compare, and evaluate three algorithms to handle the view dynamics. With extensive experiments, we demonstrate that an algorithm that exploits view locality can achieve efficient bandwidth utilization, high topology stability, and great scalability.

3D Teleimmersive (TI) environments have gained significant attention as they show promising results for a wide number of applications. These environments are usually distributed and multi platform which makes performance analysis and monitoring difficult. We present a Unified Time Service (UTS) and its measurement framework for evaluating components in terms of their reflected QoS (e.g. delay). Then we utilize UTS to analyze the delay introduced by the virtualized rendezvous points (RP) in the H-MEDIA TI system. We find that the delay overhead heavily depends on frame sizes of the 3D video streams.

Many reputation management systems have been developed under the assumption that each entity in the system will use a variant of the same scoring function. Much of the previous work in reputation management has focused on providing robustness and improving performance for a given reputation scheme. In this paper, we present a reputation-based trust management framework that supports the synthesis of trust-related feedback from many different entities while also providing each entity with the flexibility to apply different scoring functions over the same feedback data for customized trust evaluations. We also propose a novel scheme to cache trust values based on recent client activity. To evaluate our approach, we implemented our trust management service and tested it on a realistic application scenario in both LAN and WAN distributed environments. Our results indicate that our trust management service can effectively support multiple scoring functions with low overhead and high availability.

Mission-critical networks show great potential in emergency response and/or recovery, health care, critical infrastructure monitoring, etc. Such mission-critical applications demand that security service be ldquoanywhere,rdquo ldquoanytime,rdquo and ldquoanyhow.rdquo However, it is challenging to design a key management scheme in current mission-critical networks to fulfill the required attributes of secure communications, such as data integrity, authentication, confidentiality, nonrepudiation, and service availability. In this paper, we present a self-contained public key-management scheme, a scalable method of cryptographic key management (SMOCK), which achieves almost zero communication overhead for authentication, and offers high service availability. In our scheme, a small number of cryptographic keys are stored offline at individual nodes before they are deployed in the network. To provide good scalability in terms of the number of nodes and storage space, we utilize a combinatorial design of public-private key pairs, which means nodes combine more than one key pair to encrypt and decrypt messages. We also show that SMOCK provides controllable resilience when malicious nodes compromise a limited number of nodes before key revocation and renewal.

3D tele-immersion (3DTI) has recently emerged as a new way of video-mediated collaboration across the Internet. Unlike conventional 2D video-conferencing systems, it can immerse remote users into a shared 3D virtual space so that they can interact or collaborate "virtually". However, most existing 3DTI systems can support only two sites of collaboration, due to the huge demand of networking resources and the lack of a simple yet efficient data dissemination model. In this paper, we propose to use a general publish-subscribe model for multi-site 3DTI systems, which efficiently utilizes limited network resources by leveraging user interest. We focus on the overlay construction problem in the publish-subscribe model by exploring a spectrum of heuristic algorithms for data dissemination. With extensive simulation, we identify the advantages of a simple randomized algorithm. We present optimization to further improve the randomized algorithm by exploiting semantic correlation. Experimental results demonstrate that we can achieve an improvement by a factor of five.

This paper presents a novel scheme to detect copied versions of digital images based on edge features of the images. The presented scheme is the first to explore the detection of “merged copies.” To successfully detect merged copies and shifted copies, a center detection mechanism is embedded in our scheme and two kinds of clustering approaches are adopted. Experimental results confirm that our scheme not only withstands most modifications of an original image but also offers good detection performance when dealing with various rotations, 32- or 64-pixels shifting, up to 66% cropping, or merged copies. Therefore, our presented scheme will be very helpful in detecting copies of digital images transmitted in an Internet environment.

The 3D tele-immersive (3DTI) environments are emerging as the next generation technique for tele-communication. In this paper, we present a novel and generic interface for view control in 3D environments. The interface uses Wii Remote, a wireless controller for a video game console. It allows the user to manipulate the virtual camera and 3D objects intuitively with buttons and through motions (pitch and roll). We conducted user studies to evaluate the interface with a professional dancer and average audiences. The results demonstrate that the Wii Remote interface is much better suited to view control in 3DTI environments since it is much easier to use, more effective, and more accurate than the conventional mouse-based interfaces.

In this demonstration, we present WiiView, a view control interface for 3D tele-immersive (3DTI) environments. This interface allows the user to intuitively manipulate the virtual camera and graphical objects in a 3D tele-immersive virtual space, thus significantly enhancing the viewing experience for users in 3DTI collaboration. We have used WiiView in a dance class for creative choreography in Spring 2008 at University of Illinois at Urbana-Champaign. It allows for great flexibility, usability, and portability, as the dancers reported. Furthermore, the interface has become an interesting creativity tool for digital choreographers, as it creates a new element in the design space when dancers control the virtual view change and perform physical body movement simultaneously.

As Internet telephony systems continue to replace existing Public Switched Telephone Network systems, proxy servers running the Session Initiation Protocol (SIP) will continue to grow in importance for Voice-over-IP deployments that use SIP for call signaling. Since the protection of the global telecommunications infrastructure is critical to people's everyday lives, ensuring the availability of SIP proxy servers under attack should be a high priority. This paper first describes a disruptive denial-of-service attack that exploits the semantics of the SIP protocol to exhaust resources at a stateful SIP proxy server. Unlike previous approaches that focus on flooding-based denial-of-service attacks, we consider attacks that do not result in high incoming call traffic rates at the SIP proxy server. After describing this semantic-based attack, we then propose a new algorithm to reduce the effects of such an attack. Our algorithm has been implemented in a SIP proxy server and evaluated extensively through experiments on a local testbed.

We present our implementation and evaluation of TEEVE, a distributed 3D tele-immersive system. TEEVE is among the first to support multi-stream/multi-site 3D tele-immersive environments with commercial off-the-shelf hardware and software infrastructures. It promotes collaborative physical activities among geographically dispersed sites by immersing the 3D representations of remote participants into a joint 3D virtual space. In this paper, we describe our implementation of TEEVE and introduce the recent advances in its different system components. In particular, we present an implemented protocol for semantic-aware data dissemination to support multi-site remote collaboration. We evaluate the TEEVE system by deploying it on the Internet. The experimental results demonstrate that it achieves stable visual quality, soft real-time delay, and efficient resource usage.

This paper presents a cost-efficient video multicast method for live video streaming to heterogeneous mobile terminals over a content delivery network (CDN), where CDN consists of a video server, several proxies with wireless access points, and overlay links among the server and proxies. In this method, the original video sent from the server is converted into multiple versions with various qualities by letting proxies execute transcoding services based on the users' requirements, and delivered to mobile terminals along video delivery paths. To suppress the required computation and transfer costs in CDN, we propose an algorithm to calculate cost-efficient video delivery paths which minimizes the sum of the computation cost for proxies and the transfer cost on overlay links. Our basic idea for deriving cost-efficient delivery paths is to place transcoding service on different proxies in load-balancing manner, and to construct a minimal Sterner tree from all transcoding points of requested qualities. The overall goal of the placement is the balance between computation and transfer cost. Through simulations, we show that our algorithm can calculate more cost-efficient video delivery paths and achieve lower request rejections than other algorithms.

The Tele-immersive Dance Environment (TED) is a geographically distributed, real-time 3-D virtual room where multiple participants interact independent of physical distance. TED, a highly interactive collaborative environment, offers digital options with multiple viewpoints, enhancing the creative movement composition involved with dance choreography. A symbiotic relationship for creativity and design exists between dance artists and computer scientists as the tele-immersive environment is analyzed as a creativity and learning tool. We introduce the advancements of the interactive digital options, new interface developments, user study results, and the possibility of a computational model for human creativity through Laban Movement Analysis.

We demonstrate the Tele-immersive Dance Environment (TED), a geographically distributed, real-time 3-D virtual room where multiple participants interact independent of physical distance. TED, a highly interactive collaborative environment, offers digital options with multiple viewpoints, enhancing the creative movement composition involved with dance choreography. We present the advancements of the interactive digital options, new interface developments, and user study results.

3D video is an emerging technology that promises immersive experiences in a truly seamless environment. Currently, 3D video systems still require excessive bandwidth and computation power provided by gigabit switches and multi-core workstations machines. In order to extend the experience to mobile devices, we present a view-dependent compression methodology that shows great promise in making 3D video a reality on resource-constrained mobile devices. Using our technology, we are able to achieve a software-only rendering on a Nokia N800 PDA with only wireless network transmission. We believe that with the use of newer handhelds and improvements to our compression techniques, we will be able to deliver full-motion 3D video soon.

In 1969, Thomas Schelling proposed one of the most cited models in economics to explain how similar people (e.g. people with the same race, education, community) group together in American neighborhoods. Interestingly, we observe that the analogies of this model indeed exist in numerous scenarios wh

Recent large scale disasters have awaken governments at home and abroad to their needs for preparedness to support homeland security and public safety. First responders of various agencies are often on the frontline to assist in managing these events to protect lives and property. The effectiveness of their mission is highly dependent on capability of mobile wireless systems available at incident scenes, especially how their mobile infrastructure and devices can handle real-time responses and protection against threads and vulnerabilities in an integrated manner.

In this paper we present a framework for immersive virtual environment intended for remote collaboration and training of physical activities. Our multi-camera system performs full-body 3D reconstruction of human user(s) in real time and renders their image in the virtual space allowing remote users to interact. The paper features a short overview of the technology used for the capturing and reconstruction. Some of the applications where we have successfully demonstrated use of the system in combination with the tele-immersive virtual environment are described. Finally, we address current drawbacks with regard to data capturing and networking and provide some ideas for future work.

The current Internet has no secure way to validate the correctness of routing information. We propose a mechanism that supports secure validation of routing information in the interdomain routing protocol of the Internet. Our mechanism focuses on alleviating obstacles which previously prevent the complete and correct construction of the Internet routing information. In particular, we present a registry with authorized and verifiable search (RAVS) by which routing information can be constructed securely. We give an efficient RAVS scheme and prove its securities in the random oracle model. By our scheme, the routing information can be securely stored and tested without revealing contents of registry entries and search queries. Only legal autonomous systems (ASes) can construct valid registry entries and a single compromised AS can be detected. Our experiment shows that our RAVS scheme can be implemented efficiently and the incurred overhead, in terms of time and space, is acceptable in practice.

Component-based software development has evolved from a tightly coupled style to a loosely coupled style in the recent few years. The paradigm shift will eventually allow heterogeneous systems to interoperate in open networks such as the Internet and will make software development more of a management task than a development task. Envisioning that future applications may comprise dynamically aggregated component services possibly distributed widely, we develop a Quality of Service (QoS)-aware service management framework in the middleware layer to make the component services infrastructure transparent to the applications. Specifically, we manage services not only as individuals, but more importantly as meaningful aggregated entities based on the logical compositional needs coming from the applications, by composing services properly according to QoS requirements at application setup time, and performing continuous maintenance at application runtime seamlessly. Our service management framework is scalable in two dimensions: network size and application's client population size. Specifically, the framework employs a decentralized management solution that scales to large network size, and explores resource sharing in one-to-many group-based applications by means of multicasting mechanisms. Moreover, it incorporates local adaptation operations and distributed failure detection, reporting, and recovery mechanisms to deal with runtime resource fluctuations and failures.

The proliferation of multimedia applications over mobile, resource-constrained wireless networks has raised the need for techniques that adapt these applications both to clients' Quality of Service (QoS) requirements and to network resource constraints. This article investigates the upper-layer adaptation mechanisms to achieve end-to-end delay control for multimedia applications. The proposed adaptation approach spans application layer, middleware layer and network layer. In application layer, the requirement adaptor dynamically changes the requirement levels according to end-to-end delay measurement and acceptable QoS requirements for the end-users. In middleware layer, the priority adaptor is used to dynamically adjust the service classes for applications using feedback control theory. In network layer, the service differentiation scheduler assigns different network resources (e.g., bandwidth) to different service classes. With the coordination of these three layers, our approach can adaptively assign resources to multimedia applications. To evaluate the impact of our adaptation scheme, we built a real IEEE 802.11 ad hoc network testbed. The test-bed experiments show that the proposed upper-layer adaptation for end-to-end delay control successfully adjusts multimedia applications to meet delay requirements in many scenarios.

No abstract available.

Multicast is an important communication paradigm, also a problem well known for its difficulty (NP-completeness) to achieve certain optimization goals, such as minimum network delay. Recent advances in network coding has shed a new light onto this problem. In network coding, forwarding nodes can perform arbitrary operations on data received, other than forwarding or replicating, to enhance throughput of a multicast session. In this paper, we show that with the aid of network coding, the once intractable optimal multicast routing problem becomes tractable. In this problem, given a set of multicast sessions and their traffic demands, one tries to route the multicast traffic regarding various objectives, such as to minimize overall delay, or to maximize the battery life of each node. We further show that his problem can be solved in a distributed fashion: each node akes its own routing decisions based on periodic updating information from neighboring nodes. We prove that starting from any initial routing assignment, the proposed distributed routing algorithm is able to converge to the point where the value of the objective function is optimized. Our solution can be fit into a variety of networks to achieve different optimization goals. The example in this paper is maximum lifetime routing in multi-hop wireless network.

In a wireless network, data transmission suffers from varied signal strengths and channel bit error rates. To ensure successful packet reception under different channel conditions, automatic bit rate control schemes are implemented to adjust the transmission bit rates based on the perceived channel conditions. This leads to a wireless network with diverse bit rates. On the other hand, TCP is unaware of such rate diversity when it performs flow rate control in wireless networks. Experiments show that the throughput of flows in a wireless network are driven by the one with the lowest bit rate, (i.e., the one with the worst channel condition). This does not only lead to low channel utilization, but also fluctuated performance for all flows independent of their individual channel conditions. To address this problem, we conduct an optimization-based analytical study of such behavior of TCP. Based on this optimization framework, we present a joint flow control and active queue management solution. The presented channel-aware active queue management (CA-AQM) provides congestion signals for flow control not only based on the queue length but also the channel condition and the transmission bit rate. Theoretical analysis shows that our solution isolates the performance of individual flows with diverse bit rates. Further, it stabilizes the queue lengths and provides a time-fair channel allocation. Test- bed experiments validate our theoretical claims over a multi-rate wireless network testbed.

Motivated by the fact that most of the existing QoS service composition solutions have limited scalability, we develop a hierarchical-based solution framework to achieve scalability by means of topology abstraction and routing state aggregation. The paper presents and solves several unique challenges associated with the hierarchical-based QoS service composition solution in overlay networks, including topology formation (cluster detection and dynamic reclustering), QoS and service state aggregation and distribution, and QoS service path computation in a hierarchically structured network topology. In our framework, we (1) cluster network nodes based on their Internet distances and maintain clustering optimality at low cost by means of local reclustering operations when dealing with dynamic membership; (2) use data clustering and Bloom filter techniques to jointly reduce complexity of data representation associated with services within a cluster; and (3) investigate a top-down approach for computing QoS service paths in a hierarchical topology.

We present an attack containment framework against value-changing attacks in large-scale critical infrastructures, based on early warning and cooperative response approaches. We define an information structure, called attack container, which captures the trust behavior of a group of nodes and assists to contain the damage of the attack. The attack container is then used for distributed early warning and cooperative response in our framework. The simulation results show that our containment framework can detect, mitigate and contain large-scale attacks quickly.

We present a scene recognition framework for a novel automatic view management system in video conferencing. The system aims to provide the best viewpoints to the viewer based on the integration of both audio and video information. Distinguished from previous approaches, our system is aware of the 'scenes' going on, thus applying an appropriate view management policy for different scenarios is made possible. In this paper, we describe the framework of the view management system, with emphasis on the scene recognition based view management and the portability of the system. Preliminary experiments are performed for the evaluation of our approach.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

Service overlay networks (SON) provide important infrastructure support for many emerging distributed applications such as web service composition, distributed stream processing, and workflow management. Quality-sensitive distributed applications such as multimedia services and on-line data analysis often desire the SON to provide up-to-date dynamic information about different overlay nodes and overlay links. However, it is a challenging task to provide scalable and efficient information management for large-scale SONs, where both system conditions and application requirements can change over time. In this paper, we present InfoEye, a model-based self-configuring distributed information management system that consists of a set of monitoring sensors deployed on different overlay nodes. InfoEye can dynamically configure the operations of different sensors based on current statistical application query patterns and system attribute distributions. Thus, InfoEye can greatly improve the scalability of SON by answering information queries with minimum monitoring overhead. We have implemented a prototype of InfoEye and evaluated its performance using both extensive simulations and micro-benchmark experiments on PlanetLab. The experimental results show that InfoEye can significantly reduce the information management overhead compared with existing approaches. In addition, InfoEye can quickly reconfigure itself in response to application requirement and system information pattern changes.

The recent commercial availability of autostereoscopic displays has led to a rise in interest in 3D video. The need to store and transmit 3D video has created some interesting challenges. 3D video contains both color and depth information, and should be treated differently from 2D video for optimal results. This paper explores ideas for efficient 3D video source coding, tests these ideas in a human subject test with 27 subjects, and analyzes and discusses the results. It is concluded that for the specific display and codec used, 3D video file sizes can be reduced to about one-quarter of their original sizes without significant degradation in quality.

The study of 3D Tele-immersion impact on remote collaborative work represents a very interesting and challenging research topic. In this paper, we introduce the latest accomplishments of TEEVE research which merges computer science with dance choreography. This collaborative research model is ideal for creative, interdisciplinary problem solving. TEEVE offers an entirely new interface for dance choreography as a creative tool and alternative performance venue.

Tele-immersion is emerging as a new medium that creates 3D photorealistic, immersive, and interactive experience between geographically dispersed users. However, most existing tele-immersive systems can only support two-way collaboration. In this paper we propose a multi-layer framework and a new data dissemination protocol to support multi-site collaboration. The problem context is unique as multiple remote sites participate in an interactive tele-immersive session, where each site has multiple correlated 3D video streams to send (later referred as multi-stream/multi-site environments). The key challenge is to disseminate such large number of 3D live video streams among these sites subject to the bandwidth and latency constraints while satisfying QoS guarantees in visual quality. Among our findings is that the simple randomized algorithm outperforms many other static algorithms in the unique context. Moreover, the streams generated from one site have high semantic correlation, because often cameras at one site are shooting the same scene, only from different angles. We exploit the stream correlation in the multicast protocol to minimize the level of loss.

Real-time distributed multi-party/multi-stream systems are becoming more popular in many areas such as 3D tele-immersion, multi-camera conferencing and security surveillance. However, the construction of such systems in large scale is impeded by the huge demand of computing and networking resources and the lack of a simple yet powerful networking model to handle interconnection, scalability and quality of service (QoS) guarantees. We make two main contributions in the paper: (1) we propose a novel generalized ViewCast model for multi-party/multi-stream video-mediated systems that fills the gap between high-level user interest and low level per-stream management, and (2) we demonstrate the ViewCast model by applying it to the multi-party 3D Tele-Immersive (3DTI) collaboration among geographically dispersed users. More specifically, we show how the ViewCast model is used in supporting stream data dissemination, coordination and QoS management among multiple 3D tele-immersive environments. We present our experimental results in both real implementation and simulation to show that our ViewCast-based solution achieves high efficiency, scalability, and quality in supporting multi-party 3DTI collaboration.

In this work, we analyze the publish/subscribe distributed system paradigm over mobile ad hoc networks with respect to the performance and the impact of different mobility speeds and patterns of all participants in urban and high-way scenarios. In order to handle mobility, our publish/subscribe framework relies on light-weight publish/subscribe brokers, which are chosen in a topic-driven fashion. With the option of geographic location and way point information, we discuss several publish/subscribe variants and analyze their advantages/disadvantages when applied in different mobility environment. Via simulations, we measure quality of service in terms of delivery ratio and message delay achieved by such different publish/subscribe schemes over different scenarios and mobility models.

Mobile ad hoc networks show great potential in emergency response and/or recovery. Such mission-critical applications demand security service be "anywhere", "anytime" and "anyhow". However, it is challenging to design a key management scheme in current wireless ad hoc networks to fulfill the required attributes of secure communications, such as data integrity, authentication, confidentiality, non-repudiation and service availability, when Sybil attacks are present. In this paper, we present a self-contained public key management scheme, called SMOCK, which is able to resist the Sybil attack, achieves zero communication overhead for authentication, and offers high service availability. In oar scheme, small number of cryptographic keys are stored off-line at individual nodes before they are deployed in the network. To provide good scalability in terms of number of nodes and storage space, we utilize a combinatorial design of public-private key pairs, which means nodes combine more than one key pair to encrypt and decrypt messages. We also show that SMOCK provides controllable resilience when malicious nodes break into a limited number of nodes before key revocation and renewal

This paper presents results from our measurement and modeling efforts on the large-scale peer-to-peer (p2p) overlay graphs spanned by the PPLive system which is arguably the most popular and largest multimedia streaming p2p system today. We believe that our findings can be used to understand large-scale p2p streaming systems for future planning of resource usage, and to provide useful and practical hints for future design of large-scale p2p streaming systems. Unlike other previous studies on PPLive, which focused on either network-centric or user-centric measurements of the system, our study is unique in (a) focusing on PPLive overlay-specific characteristics, and (b) being the first to derive mathematical models for its distributions of channel population size and session length.

The ten papers in this special issue are devoted to peer-to-peer video streaming.

Due to the shared medium nature of wireless networks, the uncertainties caused by collisions and interferences make the quality of service (QoS) issue harder than its wired counterpart. Many publications have been focused on network and MAC layer design to address the QoS issue in wireless networks. However, the middleware design has been overlooked. For QoS support, we need to map the QoS requirement of applications to performance metrics. Middleware is the place we do such mapping. In this paper, we use packet level priority to bridge the QoS requirements and performance. Through middleware priority adaptation, we aim to make the premium traffic meet the QoS requirement, we study the impact of middleware priority adaptation on QoS performance, including bandwidth and end- to-end delay, via experiments with multimedia flows over IEEE 802.11 environment. Our evaluation is based on experiments in both WLAN and ad hoc network environment. Our investigation shows that middleware adaptation is efficient in assisting to achieve QoS in many scenarios.

The current Internet has no secure way to validate the correctness of the routing information. We suggest a mechanism that supports secure validation of routing information in the interdomain routing protocol of the Internet. Our mechanism focuses on alleviating obstacles which previously prevent the complete and correct construction of the Internet routing information. In particular, we propose an identity-based Registry with Authorized and Verifiable Search (RAVS) so that routing information can be constructed securely. We construct an efficient RAVS scheme and prove its securities in the random oracle model. By our scheme, the routing information can be securely stored and tested without revealing contents of both the registry and the search query. Furthermore, our registry is verifiable and its correctness is guaranteed. Only the legal autonomous system (AS) can construct the valid registry and the single compromised AS can be detected. Our experiment shows that our RAVS scheme can be implemented efficiently and the incurred overhead, in terms of time and space, is acceptable in practice.

Many emerging applications for peer to peer overlays may require nodes to satisfy strict timing deadlines to access a replica of a given object. This includes multimedia and hard realtime applications such as distributed gaming. We formulate the QoS-aware replication problem, the goal of which is to locate the minimum number of replicas to satisfy access time deadlines for all nodes while minimizing storage usage in the overlay. Existing replication schemes cannot be used to solve this problem since they are best-effort only. We show that finding a solution to the QoS-aware object replication in an arbitrary overlay topology is intractable (NP-complete). We then present simple centralized as well as decentralized heuristics for QoS-aware replication, and compare their performance experimentally. In addition, we investigate how these decentralized heuristics effectively works in a real network.

In multi-site distributed education (MSDE), video streams from multiple sites are available. To best utilize the limited screen space at each site, we develop a customizable, automated display management system in this paper, i.e., only user-preferred streams will be shown as triggered by events and timers. The configuration of such user preference, however, is challenging because it has to be both human-friendly and machine-friendly. To address this challenge, we propose a three-layer virtual director model. In the user layer, we identify three categories of parameters that can represent a wide range of user preferences yet are easy to use. These preferences are then automatically translated into a machine-friendly timed automaton in the execution layer. The automaton is simulated dynamically, which selects a subset of streams to show on the screen through a display layer. Evaluation results demonstrate the correctness and efficiency of the proposed framework

Many Peer-to-peer (P2P) applications such as media broadcasting and content distribution require a high performance overlay structure in order to deliver satisfying quality of service (QoS). Previous approaches to building such overlays either involve a shared contact point, which results in non-scalable solutions, or rely on gossip style membership dissemination, which lacks QoS awareness. In this paper, we present a distributed membership service called RandPeer, which manages membership information on behalf of P2P applications, and allows peers to locate good neighbors based on their QoS characteristics. Using this service, P2P applications can easily construct their overlays in a scalable and QoS aware fashion. We have implemented RandPeer and experimented in both local and wide area environments. Our results show that (1) RandPeer is scalable and robust to highly dynamic P2P memberships; (2) RandPeer has good lookup performance, both in terms of response time and the randomness of peer selection. The latter improves load balancing and failure resilience of P2P applications; (3) when used to improve the performance of a mesh based P2P overlay, RandPeer achieves 10 % improvement in just 2 protocol rounds, which is more than 5 times faster than pure random neighbor selections. 1

Abstract — Service Composition is a promising technology for providing on-demand composed services in dynamic and loosely coupled peer-to-peer (P2P) networks. Because of system dynamics, such as the peer leaving from the system, end users may perceive interference from service disruptions. How to minimize the userperceived interference and provide Quality of Service guarantees to the composite services thus becomes important and challenging. In this paper, we take a novel approach to study the problem of minimum interference service composition with QoS guarantees. First, we propose a general analytic framework to model the interference and its intensity to the end users due to system dynamics. Based on this framework, the minimum interference routing problem is formulated. Then, we present an optimal solution to the problem through dynamic programming and investigate some optimization simplifications in special cases. We further propose a heuristic measure for fast interference calculation and design efficient routing algorithms by exploiting the local path recovery and reliable service paths. Our analysis and extensive simulations demonstrate that our model and algorithms can achieve much better performance than the traditional methods in finding service paths, with respect to decreasing the interference to end users, especially in the scenarios of stringent QoS requirement, highly dynamic networks, or the type of impatient users. I.

We first present the tele-immersive environments developed jointly by University of Illinois at Urbana-Champaign and University of California at Berkeley. The environment features 3D full and real body capturing, wide field of view, multi-display 3D rendering, and attachment free participant. We then describe a study of collaborative dancing between remotely located dancers in the shared virtual space. Two professional dancers are invited to the tele-immersive site of each university. As a preliminary experiment, we let the dancers perform elementary body movements and coordinate their dancing. The coordination requires one dancer to take the lead while the other follows her by appropriate movements. During the experiment, the dancers are dancing at various motion rates to evaluate how well the collaborative dancing is supported with the current technical boundary. Our important findings indicate that 1) tele-immersive environments have strong potential impact on the concept of choreography and communication of live dance performance, 2) the presence of multi-display system, real body 3D rendering, audio channel, and less intrusive-ness greatly enhances the immersive and dancing experience, and 3) the level of synchronization achieved by the dancers is higher than that expected from the video rate

Large-scale distributed systems, e.g., grid or P2P networks, are targets for large-scale attacks. Unfortunately, few existing systems support propagation of alerts during the attack itself while also suppressing disruptive alerts from faulty or malicious sources. This paper proposes the "ContagAlert" protocol, which uses contagion spreading behavior to spread alerts. ContagAlert rapidly propagates alerts during attacks while also suppressing disruptive alerts. The core contagion protocols in the system are completely localized, but result in desired behavior at the network scale. We analyze and evaluate our protocol with synthetic simulations and in both Internet worm and DoS attack scenarios

Tele-immersive environments will improve the state of collaboration among distributed participants. However, along with the promise a new set of challenges have emerged including the real-time acquisition, streaming and rendering of 3D scenes to convey a realistic sense of immersive spaces. Unlike 2D video conferencing, a 3D tele-immersive environment employs multiple 3D cameras to cover a much wider field of view, thus generating a very large volume of data that need to be carefully coordinated, organized, and synchronized for Internet transmission, rendering and display. This is a challenging task and a dynamic bandwidth management must be in place. To achieve this goal, we propose a multi-stream adaptation framework for bandwidth management in 3D tele-immersion. The adaptation framework relies on the hierarchy of mechanisms and services that exploits the semantic link of multiple 3D video streams in the tele-immersive environment. We implement a prototype of the framework that integrates semantic stream selection, content adaptation, and 3D data compression services with user preference. The experimental results have demonstrated that the framework shows a good quality of the resulting composite 3D rendered video in case of sufficient bandwidth, while it adapts individual 3D video streams in a coordinated and user-friendly fashion, and yields graceful quality degradation in case of low bandwidth availability.

Wireless networks are accommodating more and more emerging multimedia traffic from entertainment to telephony. However, wireless network standards typically focus on MAC layer and physical layer signaling, leaving crucial end to end delay and security issues to application implementers. Delay and security provisioning issues in such an environment is very challenging. In this paper, we address an integrated solution to end-to-end delay and security, where the middleware adaptation provides tunable delay and security support according to network condition. The test-bed experiments show that the proposed solution successfully adjusts multimedia applications to meet both delay and security requirements in IEEE 802.11-based wireless environment

Abstract Emerging large‐scale utility computing systems such as Grids promise computing and storage to be provided to end users as a utility. System management services deployed in the middleware are a key to enabling this vision. Utility Grids provide a challenge in terms of scale, dynamism and heterogeneity of resources and workloads. In this paper, we present a model‐based architecture for resource allocation services for Utility Grids. The proposed service is built in the context of interactive remote desktop session workloads and takes application performance QoS models into consideration. The key design guidelines are hierarchical request structure, application performance models, remote desktop session performance models, site admission control, multi‐variable resource assignment system and runtime session admission control. We have also built a simulation framework that can handle mixed batch and remote desktop session requests, and have implemented our proposed resource allocation service into the framework. We present some results from experiments using the framework. Our proposed architecture for resource allocation services addresses the needs of emerging utility computing systems and captures the key concepts and guidelines for building such services in these environments. Copyright © 2005 John Wiley & Sons, Ltd.

The shared-medium multihop nature of wireless ad hoc networks poses fundamental challenges to the design of effective resource allocation algorithms that are optimal with respect to resource utilization and fair across different network flows. None of the existing resource allocation algorithms in wireless ad hoc networks have realistically considered end-to-end flows spanning multiple hops. Moreover, strategies proposed in wireline networks are not applicable in the context of wireless ad hoc networks, due to their unique characteristics of location-dependent contention. In this paper, we propose a new price-based resource allocation framework in wireless ad hoc networks to achieve optimal resource utilization and fairness among competing end-to-end flows. We build our pricing framework on the notion of maximal cliques in wireless ad hoc networks, as compared to individual links in traditional wide-area wireline networks. Based on such a price-based theoretical framework, we present a two-tier iterative algorithm. Distributed across wireless nodes, the algorithm converges to a global network optimum with respect to resource allocations. We further improve the algorithm toward asynchronous network settings and prove its convergence. Extensive simulations under a variety of network environments have been conducted to validate our theoretical claims.

Mobile devices primarily processing multimedia data need to support multimedia quality with limited battery energy. To address this challenging problem, researchers have introduced adaptation into multiple system layers, ranging from hardware to applications. Given these adaptive layers, a new challenge is how to coordinate them to fully exploit the adaptation benefits. This paper presents a novel cross-layer adaptation framework, called GRACE-1, that coordinates the adaptation of the CPU hardware, OS scheduling, and multimedia quality based on users' preferences. To balance the benefits and overhead of cross-layer adaptation, GRACE-1 takes a hierarchical approach: It globally adapts all three layers to large system changes, such as application entry or exit, and internally adapts individual layers to small changes in the processed multimedia data. We have implemented GRACE-1 on an HIP laptop with the adaptive Athlon CPU, Linux-based OS, and video codecs. Our experimental results show that, compared to schemes that adapt only some layers or adapt only to large changes, GRACE-1 reduces the laptop's energy consumption up to 31.4 percent while providing better or the same video quality.

Service composition allows multimedia services to be automatically composed from atomic service components based on dynamic service requirements. Previous work falls short for distributed multimedia service composition in terms of scalability, flexibility and quality-of-service (QoS) management. In this paper, we present a fully decentralized service composition framework, called SpiderNet, to address the challenges. SpiderNet provides statistical multiconstrained QoS assurances and load balancing for service composition. Moreover, SpiderNet supports directed acyclic graph composition topologies and exchangeable composition orders. We have implemented a prototype of SpiderNet and conducted experiments on both wide-area networks and a simulation testbed. Our experimental results show the feasibility and efficiency of the SpiderNet service composition framework.

This article presents the design, implementation, and evaluation of EScheduler , an energy-efficient soft real-time CPU scheduler for multimedia applications running on a mobile device. EScheduler seeks to minimize the total energy consumed by the device while meeting multimedia timing requirements. To achieve this goal, EScheduler integrates dynamic voltage scaling into the traditional soft real-time CPU scheduling: It decides at what CPU speed to execute applications in addition to when to execute what applications. EScheduler makes these scheduling decisions based on the probability distribution of cycle demand of multimedia applications and obtains their demand distribution via online profiling.We have implemented EScheduler in the Linux kernel and evaluated it on a laptop with a variable-speed CPU and typical multimedia codecs. Our experimental results show four findings: first, the cycle demand distribution of our studied codecs is stable or changes slowly. This stability implies the feasibility to perform our proposed energy-efficient scheduling with low overhead. Second, EScheduler delivers soft performance guarantees to these codecs by bounding their deadline miss ratio under the application-specific performance requirements. Third, EScheduler reduces the total energy of the laptop by 14.4% to 37.2% relative to the scheduling algorithm without voltage scaling and by 2% to 10.5% relative to voltage scaling algorithms without considering the demand distribution. Finally, EScheduler saves energy by 2% to 5% by explicitly considering the discrete CPU speeds and the corresponding total power of the whole laptop, rather than assuming continuous speeds and cubic speed-power relationship.

Although initially proposed as the deployable alternative to IP multicast, the overlay network actually revolutionizes the way network applications can be built. In this paper, we study the rate allocation problem in overlay-based multirate multicast, which can be understood as a utility-based resource allocation problem. Each receiver is associated with a utility defined as a function of its streaming rate. Our goal is to maximize the aggregate utility of all receivers, subject to network capacity constraint and data constraint. The latter constraint is unique in overlay multicast, mainly due to the dual role of end hosts as both receivers and senders. We use a price-based approach to address this problem. Two types of prices, network price and data price, are generated with regard to the two constraints of the problem. A distributed algorithm is proposed, where each receiver adjusts its flow rate according to the associated network price and data price. The algorithm is proved to converge to the optimal point, where the aggregate utility of all receivers is maximized. We implement our algorithm using an end-host-based protocol. Our protocol purely relies on the coordination of end hosts to accomplish tasks originally assigned to network routers, which makes it directly deployable to the existing network infrastructure.

Stream processing has become increasingly important as many emerging applications call for continuous real-time processing over data streams, such as voice-over-IP telephony, security surveillance, and sensor data analysis. In this paper, we propose a composable stream processing system for cooperative peer-to-peer environments. The system can dynamically select and compose stream processing elements located on different peers into user desired applications. We investigate multiple alternative approaches to composing stream applications: 1) global-state-based centralized versus local-state-based distributed algorithms for initially composing stream applications at setup phase. The centralized algorithm performs periodical global state maintenance while the distributed algorithm performs on-demand state collection. 2) Reactive versus proactive failure recovery schemes for maintaining composed stream applications during runtime. The reactive failure recovery algorithm dynamically recomposes a new stream application upon failures while the proactive approach maintains a number of backup compositions for failure recovery. We conduct both theoretical analysis and experimental evaluations to study the properties of different approaches. Our study illustrates the performance and overhead trade-offs among different design alternatives, which can provide important guidance for selecting proper algorithms to compose stream applications in cooperative peer-to-peer environments.

Abstract Cooperation in wireless ad hoc networks has twofold implications. First, each wireless node does not excessively and greedily inject traffic to the shared wireless channel. Second, intermediate nodes voluntarily relay traffic for upstream nodes towards the destination at the cost of its own private resource. Such an assumption supports almost all existing research when it comes to protocol design in ad hoc networks. We believe that without appropriate incentive mechanisms, the nodes are inherently selfish (unwilling to contribute its private resource to relay traffic) and greedy (unfairly sharing the wireless channel). In this paper, we present a price pair mechanism to arbitrate resource allocation and to provide incentives simultaneously such that cooperation is promoted and the desired global optimal network operating point is reached by convergence with a fully decentralized self‐optimizing algorithm. Such desired network‐wide global optimum is characterized with the concept of Nash bargaining solution (NBS), which not only provides the Pareto optimal point for the network, but is also consistent with the fairness axioms of game theory. We simulate the price pair mechanism and report encouraging results to support and validate our theoretical claims. Copyright © 2006 John Wiley & Sons, Ltd.

Stream processing has become increasingly important with emergence of stream applications such as audio/video surveillance, stock price tracing, and sensor data analysis. A challenging problem is to provide optimal component composition in a distributed stream processing environment. The goal of optimal component composition is to achieve load balancing subject to multiple function, resource, and quality-of-service (QoS) constraints while composing stream applications. In this paper, we present an adaptive composition probing (ACP) approach to the problem. Different from previous work, ACP provides a new hybrid approach that combines distributed composition probing with coarse-grain global state management. Guided by the coarse-grain global state information, ACP selectively probes a subset of candidate components to discover an approximately optimal component composition. Further, ACP is self-tuning, which can adoptively adjust the number of probes to maintain a specified composition performance target (i.e., composition success rate) in a dynamic stream environment. While the optimal component composition problem is NP-hard, our ACP approach provides an adaptive polynomial approximation solution. We have conducted extensive simulation experiments to show the efficiency, scalability, and adaptability of the ACP approach by comparing with other alternative solutions

In this paper, we propose RAVITAS, a framework for realistic voice chat among multiple users in a virtual space reproducing the cocktail party effect. RAVITAS utilizes context-aware voice filtering (CAVF), pub/sub-based locality management, and controlled voice streaming to achieve this effect. Our preliminary experiments show that RAVITAS achieves satisfactory perception-based subjective results for a small group of users.

Tele-immersive 3D multi-camera room environments are starting to emerge and with them new challenging research questions. One important question is how to organize the large amount of visual data, being captured, processed, transmitted and displayed, and their corresponding resources, over current COTS computing and networking infrastructures so that "everybody" would be able to install and use tele-immersive environments for conferencing and other activities. In this paper, we propose a novel cross-layer control and streaming framework over general purpose delivery infrastructure, called TEEVE (tele-immersive environments for everybody). TEEVE aims for effective and adaptive coordination, synchronization, and soft QoS-enabled delivery of tele-immersive visual streams to remote room(s). The TEEVE experiments between two tele-immersive rooms residing in different institutions more than 2000 miles apart show that we can sustain communication of up to 12 3D video streams with 4/spl sim/5 3D frames per second for each stream, yielding 4/spl sim/5 tele-immersive video rate.

10.1145/1101149.1101297

Multihop wireless hotspot network [A. Balachandran et al., (2003), F. Fitzek et al., (2003), Y-D. Lin et al., (1999), K-C. Wang et al., (2003)] has been recently proposed to extend the coverage area of a base station. However, with selfish nodes in the network, multihop packet forwarding cannot take place without an incentive mechanism. In this paper, we adopt the "pay for service" incentive model, i.e., clients pay the relaying nodes for their packet forwarding service. Our focus in this paper is to determine a "fair" pricing for packet forwarding. To this end, we model the system as a market where the pricing for packet forwarding is determined by demand and supply. Depending on the network communication scenario, the market models are different. We classify the network into four different scenarios and propose different pricing mechanisms for them. Our simulation results show that the pricing mechanisms are able to guide the market into an equilibrium state quickly. We also show that maintaining communication among the relaying nodes is important for a stable market pricing.

We present a new feedback control scheme for resource allocation in wireless ad hoc environment. The congestion measure on a wireless link is symbolized as a virtual price, which depends on the recent history of wireless node's queue size. The aggregate price along a route is the feedback to control the transmission rate at the source. In this way, the transmission rate adjustment will dynamically achieve efficient resource allocation, and make the queue size on wireless links stabilize around desired equilibrium. Simulation results show that the proposed scheme can not only help allocate bandwidth fairly, but also achieve the goal of optimal sharing the channel capacity among multi-hop flows.

Wireless 2D cameras are becoming more widely used for applications such as video surveillance and conferencing due to their easy deployment. These scenarios require multiple high quality video streams that share limited wireless channel resource. Therefore, a bandwidth management that is sensitive to application QoS requirements, content extraction and the specifics of a camera array environment is essential. This paper addresses the problem of bandwidth management to coordinate multiple video flows and to support streaming from wireless camera array. We present a bandwidth management framework that deploys a coordination scheme between the camera array and system resources. Especially, the framework explores different relations and scheduling policies between cameras and the bandwidth allocation to achieve better multi-view video delivery. The implementation uses Linux platform and IEEE 802.11b wireless ad hoc network. Our experimental results show that the bandwidth management framework helps achieving streaming differentiation while maintaining high quality video delivery.

Energy efficient routing is a critical problem in multihop wireless networks due to the severe power constraint of wireless nodes. Despite its importance and many research efforts towards it, a distributed routing algorithm that maximizes network lifetime is still missing. To address this problem, we propose a novel utility-based nonlinear optimization formulation to the maximum lifetime routing problem. Based on this formulation, we further present a fully distributed, localized routing algorithm, which is proved to converge to the optimal point, where the network lifetime is maximized. Solid theoretical analysis and simulation results are presented to validate our solution.

The recent deployment of large distributed computing systems such as content distribution networks and the Planet-Lab has made it possible for researchers and practitioners to experiment with real world, large scale distributed applications. However, running an application in such an environment is difficult, due to the scale and frequent node failures of such systems. Thus, an important tool is needed that helps application developers/deployers to manage their applications. Our goal in this work is to develop MON, an extremely lightweight and failure resilient system for managing distributed applications. MON allows users to execute instant management commands on the distributed computing nodes, such as query the current status of the application, or start/stop a process on the distributed nodes. The commands are propagated to all the nodes and executed on each node, and the results are aggregated and returned back. We believe the ability to execute such instant commands is especially useful for the initial deployment of a distributed application, or for the monitoring and diagnoistics of (unexpected) application failures.

In This work we investigate the utilization of non-dedicated, opportunistic resources in a desktop environment to provide statistical assurances to a class of QoS sensitive, soft real-time applications. Supporting QoS in such an environment presents unique challenges: (1) soft real-time tasks must have continuous access to resources in order to deliver meaningful services. Therefore the tasks will fail if not enough idle resources are available in the system. (2) Although soft real-time tasks can be migrated from one machine to another, their QoS may be affected if there are frequent migrations. In this paper, we define two new QoS metrics (task failure rate and probability of bad migrations) to characterize these QoS failures/degradations. We also design admission control and resource recruitment algorithms to provide statistical guarantees on these metrics. Our model based simulation results show that the admission control algorithms are effective at providing the desired level of assurances, and are robust to different resource usage patterns. Our resource recruitment algorithm may need long time of observations to provide the desired guarantee. But even with moderate observations, we can reduce the probability of a bad migration from 12% to less than 4%, which is good enough for most real applications.

Resource prediction can greatly assist resource selection and scheduling in a distributed resource sharing environment such as a computational Grid. Existing resource prediction models are either based on the auto-correlation of a single resource or based on the cross correlation between two resources. In this paper, we propose a multi-resource prediction model (MModel) that uses both kinds of correlations to achieve higher prediction accuracy. We also present two adaptation techniques that enable the MModel to adapt to the time-varying characteristics of the underlying resources. Experimental results with CPU load prediction in both workstation and Grid environment show that on average, the adaptive MModel (called MModel-a) can achieve from 6% to more than 96% reduction in prediction errors compared with the autoregressive (AR) model, which has previously been shown to work well for CPU load predictions.

Utility Grids implement a virtualization architecture and allow for sharing of infrastructure for improved return on investment (ROI). We consider extending the existing Grid infrastructure to support interactive sessions in an enterprise setting. This would allow users to remotely execute interactive applications in the Grid and view their output using remote display technologies. We propose a resource allocation architecture to support such interactive sessions in utility Grids. End-users submit requests for a remote desktop session. A utility Grid site resource allocation system assigns a compute node for this request considering performance needs of such remote desktop sessions. We also propose a hierarchical admission control system. The system consists of a site admission control system for admission control of remote desktop sessions, and a session admission control system at the compute node for admission check of per-application interactive sessions. We also present discussion on mixed workloads consisting of requests for batch jobs and interactive remote desktop sessions.

Based on the distributed and composable services model, the QoS service routing/composition problem has emerged as the middleware support for multimedia applications. Different from the conventional QoS data routing, QoS service routing presents additional challenges caused by the service functionality, service dependency, resource requirement heterogeneity, and loop issues that make solutions for QoS data-routing inapplicable to QoS service routing. Existing solutions for addressing this problem are either not generic enough or not integrated, so that they either become inapplicable to new environments/metrics or the computed paths are sub-optimal. This paper presents a generic and integrated approach for computing optimal service paths, and shows an aggregate performance function - F- that optimizes several QoS metrics at the same time. Simulations show that F is superior, and integrating service configuration selection with service path finding is desirable.

Providing service differentiation in wireless networks has attracted much attention in recent research. Existing studies so far have focused on the design of MAC layer protocols to achieve distributed priority scheduling. While these works have been shown to achieve certain differentiation (higher throughput or lower delay for higher priority packets) at MAC layer, they did not consider the problem of service differentiation from end-to-end perspective. Moreover, these approaches did not present a formalized service differentiation model that quantifies the degree of differentiation, which is critical for service class selection and provision. This paper addresses above problems by introducing a clear service differentiation model, proportional service differentiation, to the domain of wireless network which targets at providing proportional delay differentiation in wireless LAN. Due to the unique characteristic of distributed medium sharing, the scheduling algorithm employed in wireline networks can not be applied directly to the context of the wireless network. We argue that delay differentiation in wireless LAN can be only achieved through a joint packet scheduling at network layer and distributed coordination at MAC layer. Hereby, we present a distributed waiting time priority scheduling (DWTP) algorithm which is deployed on a cross-layer architecture. DWTP consists of two tiers: an intra-node predictive WTP scheduler at network layer and an inter-node priority-based distributed coordination at MAC layer. These two tiers coordinate via cross-layer priority mapping to achieve proportional delay differentiation. Simulation results show that the cross-layer DWTP algorithm can effectively achieve proportional delay differentiation in wireless LAN.

In a public mobile ad hoc network (MANET), users may be selfish and refuse to forward packets for other users. Therefore, an incentive mechanism must be in place. We adopt the "pay for service" model of cooperation, and propose an auction-based incentive scheme (called iPass) to enable cooperative packet forwarding behavior in MANET. Each flow pays the market price of packet forwarding service to the intermediate routers. The resource allocation mechanism in our scheme is based on the generalized Vickrey auction with reserve pricing. We prove that in our scheme, user's truthful bidding of utility remains a dominant strategy, users and routers have incentive to participate in the scheme, and packet forwarding always leads to higher social welfare for the whole network. We design a signaling protocol to implement the scheme, and show that it can serve as an explicit rate-based flow control mechanism for the network. Therefore, iPass is a joint solution of incentive engineering and flow control in a noncooperative MANET. Simulation results show that iPass is able to determine the auction outcome quickly, and at the same time achieve the goals of flow control.

Ubiquitous IP telephony has become a feasible Internet service, and it is expected to meet the quality standards of traditional telephone services. The work presents a distributed voice-over-IP (VoIP) conferencing system called Venus that is implemented as a composable application-level service overlay network. Compared to the traditional centralized approach, Venus achieves better scalability and resource utilization by efficiently aggregating resources across distributed voice mixers. Moreover, Venus provides multi-constrained quality-of-service (QoS) provisioning by establishing each conferencing session based on multiple QoS constraints (e.g., delay, loss rate) and resource requirements (e.g., bandwidth, audio channels). Venus provides a failure resilient VoIP conferencing service by leveraging the fast failure recovery capability of the application-level service overlay network. Large-scale simulation results illustrate the efficiency of the Venus system.

The performance of global Internet communication is significantly influenced by the reliability and the stability of Internet routing systems, especially the border gateway protocol (BGP), the de facto standard for inter-domain routing. We investigate the reliability of BGP sessions and the internal BGP (IBGP) networks in the environment of unreliable physical and routing layers. The reliability analysis of IBGP networks is difficult, because IBGP sessions may be correlated to each other by the shared underlying physical links and TCP enables IBGP sessions to tolerate certain level of network failures. We first investigate the failure probability of IBGP sessions and its relation to BGP timers and TCP retransmission behaviors. The result of this investigation is a simple modification of TCP that increases the robustness of IBGP sessions significantly. Second, we present a novel reliability model to measure the resilience of the whole IBGP networks. This model is of great importance for studying the function loss of IBGP operations and it also provides the theory basis for IBGP network optimization in terms of reliability.

The QoS service routing problem has recently emerged as a consequence of the increasingly accepted distributed and composable services model. Different from the conventional QoS data routing, QoS service routing presents additional challenges caused by the service functionality, service dependency, resource requirement heterogeneity, and loop formation issues, that make solutions for QoS data routing inapplicable to QoS service routing. We study this problem both in one-to-one and one-to-many application scenarios, so that despite the fact that the component services are located distributively in multiple hosts, the system can still provide integrated services seamlessly and efficiently.

Although initially proposed as the deployable alternative to IP multicast, overlay multicast actually offers us great flexibilities on QoS-aware resource allocation for network applications. For example, in overlay multicast streaming, (1) the streaming rate of each client can be diversified to better accommodate network heterogeneity, through various end-to-end streaming adaptation techniques; and (2) one can freely organize the overlay session by rearranging the multicast tree, for the purpose of better resource utilization and fairness among all clients. The goal of this paper, is to find the max-min rate allocation in overlay multicast, which is pareto-optimal in terms of network resource utilization, and max-min fair. We approach this goal in two steps. First, we present a distributed algorithm, which is able to return the max-min rate allocation for any given overlay multicast tree. Second, we study the problem of finding the optimal tree, whose max-min rate allocation is optimal among all trees. After proving its NP-hardness, we propose a heuristic algorithm of overlay multicast tree construction. A variation of the heuristic is also designed to handle the dynamic client join/departure. Both of them have approximation bound of 1/2 to the optimal value. Experimental results show that they achieve high average throughput, almost saturate link utilization, and consistent min-favorability.

The realization of multimedia systems still heavily relies on building monolithic systems that need to be reengineered for every change in the application and little of which can be reused in subsequent developments even for similar applications. Hence, building complex large scale multimedia systems is still a difficult and challenging problem. Service-based architectures, like researched in the Web community, form a possible solution to this problem: The service-based paradigm decomposes complex tasks into smaller independent entities (e.g. Web services), and then supports a flexible service composition in a variety of ways. However, due to the characteristics of multimedia applications and rich semantic structure of multimedia data and workflows, a direct application of Web-based research results is still difficult. The reason is that Web service frameworks cannot yet cope with the complexity of multimedia applications and their metadata. In this paper, we describe a basic taxonomy for the composition of services to support complex multimedia workflows. We will investigate in detail the necessary steps and methodology for multimedia service compositions and apply our taxonomy to different service composition instances. We will illustrate all composition instances within our taxonomy with case studies and point to possible techniques for the composition problem.

This paper presents the design, implementation, and evaluation of a practical voltage scaling (PDVS) algorithm for mobile devices primarily running multimedia applications. PDVS seeks to minimize the total energy of the whole device while meeting multimedia timing requirements. To do this, PDVS extends traditional real-time scheduling by deciding what execution speed in addition to when to execute what applications. PDVS makes these decisions based on the discrete speed levels of the CPU, the total power of the device at different speeds, and the probability distribution of CPU demand of multimedia applications. We have implemented PDVS in the Linux kernel and evaluated it on an HP laptop. Our experimental results show that PDVS saves energy substantially without affecting multimedia performance. It saves energy by 14.4% to 37.2% compared to scheduling algorithms without voltage scaling and by up to 10.4% compared to previous voltage scaling algorithms that assume an ideal CPU with continuous speeds and cubic power-speed relationship.

In this paper, we examine the problem of large-volume data dissemination via overlay networks. A natural way to maximize the throughput of an overlay multicast session is to split the traffic and feed them into multiple trees. While in single-tree solutions, bandwidth of leaf nodes may remain largely under-utilized, multi-tree solutions increase the chances for a node to contribute its bandwidth by being a relaying node in at least one of the trees. We study the following problems: (1) What is the maximum capacity multi-tree solutions can exploit from overlay networks? (2) When multiple sessions compete within the same network, what is the relationship of two contradictory goals: achieving fairness and maximizing overall throughput? (3) What is the impact of IP routing in achieving at constraining the optimal performance of overlay multicast.We extend the multicommodity flow model to the case of overlay data dissemination, where each commodity is associated with an overlay session, rather than the traditional source-destination pair. We first prove that the problem is solvable in polynomial time, then propose an ε-approximation algorithm, assuming that each commodity can be split in arbitrary ways. The solution to this problem establishes the theoretical upper bound of overall throughput that any multi-tree solution could reach. We then study the same problem with the restriction that each commodity can only be split and fed into a limited number of trees. A randomized rounding algorithm and an online tree-construction algorithm are presented. All these algorithms are evaluated by extensive simulations.

Flow control in a mobile ad hoc network (MANET) must face many new challenges such as frequent rerouting and bandwidth variation of the wireless links. TCP's implicit AIMD flow control performs poorly in this environment, because it often cannot keep up with the dynamics of the network. This paper explores the potential utility of explicit flow control in the MANET domain. To this end, we propose an end-to-end rate-based flow control scheme (called EXACT), where a flow's allowed rate is explicitly conveyed from intermediate routers to the end-hosts in each data packet's special control header. As a result, EXACT reacts quickly and precisely to re-routing and bandwidth variation, which makes it especially suitable for a dynamic MANET network and also discusses several supporting mechanisms required for such a scheme at the MAC and the transport layers. By ns-2 simulations, we show that EXACT outperforms TCP in terms of fairness and efficiency, especially in a highly dynamic MANET environment.

Although initially proposed as the deployable alternative to Internet protocol multicast, the application-layer overlay network actually revolutionizes the way network applications can be built, since each overlay node is an end host and is able to carry out more functions than simply forwarding packets. The paper addresses the on-demand media distribution problem in the context of an overlay network. We take advantage of the strong buffering capabilities of end hosts, and propose a novel overlay multicast strategy, oStream. We have performed extensive analysis and performance evaluation with respect to the scalability and the efficiency of oStream. With respect to the required server bandwidth, we show that oStream defeats the theoretical lower bound of traditional multicast-based approaches, under both sequential and nonsequential stream access patterns. oStream is also robust against bursty requests. With respect to bandwidth consumption on the backbone network, we show that the benefit introduced by oStream overshadows the topological inefficiency (e.g., link stress and stretch) introduced by using application-layer multicast.

To enable end-to-end quality-of-service (QoS) guarantees in the Internet, based on the border gateway protocol (BGP), interdomain QoS information advertising, and routing are important. However, little research has been done in this area so far. Two major challenges, scalability and heterogeneity, make the QoS extension to BGP difficult. In the existing routing schemes, static and instantaneous QoS metrics, such as link capacity and available bandwidth, are used to represent QoS routing information, but neither of them can solve the two challenges well. In this paper, BGP is extended to advertise available bandwidth and delay information of routes, but, instead of using the traditional deterministic metrics, a series of statistical metrics, available bandwidth index (ABI), delay index (DI), available bandwidth histogram (ABH), and delay histogram (DH), are defined and applied to QoS information advertising and routing. Two major contributions of the proposed statistical metrics are: 1) QoS information is abstracted into one or several probability intervals and, thus, the heterogeneous and dynamic QoS information can be represented more flexibly and precisely and 2) by capturing the statistical property of the detailed distribution of QoS information, these new metrics are efficient and they can highly decrease the message overhead in routing, thereby making the QoS advertising and routing scalable. Our extensive simulations confirm both contributions of the QoS extension to BGP very well. Moreover, besides BGP, these statistical metrics can be applied to other networks and protocols to represent QoS information in a more scalable and precise way.

Routing is a process of finding a network path from a source node to a destination node. The execution time and the memory requirement of a routing algorithm increase with the size of the network. In order to deal with the scalability problem, large networks are often structured hierarchically by grouping nodes into different domains. The internal topology of each domain is then aggregated into a simple topology that reflects the cost of routing across that domain. This process is called topology aggregation. For delay-bandwidth sensitive networks, traditional approaches represent the property of each link in the aggregated topology as a delay-bandwidth pair, which corresponds to a point on the delay-bandwidth plane. Since each link after aggregation may be the abstraction of many physical paths, a single delay-bandwidth pair results in significant information loss. The major contribution of this paper is a novel quality-of-service (QoS) parameter representation with a new aggregation algorithm and a QoS-aware routing protocol. Our QoS representation captures the state information about the network with much greater accuracy than the existing algorithms. Our simulation results show that the new approach achieves very good performance in terms of delay deviation, success ratio, and crankback ratio.

Abstract Providing service differentiation in wireless networks has attracted much attention in recent research. Existing studies so far have focused on the design of differentiated media access algorithms. Some quality of service (QoS) metrics, such as queueing delay cannot be completely addressed by these approaches. Moreover, without a formalized service differentiation goal that quantifies the outcome of differentiation, the performance of most of the approaches fluctuates, especially in short time‐scales. This paper addresses above problems by introducing the concept of proportional service differentiation, to the domain of wireless network and focuses on providing proportional delay differentiation in wireless LANs. Due to the unique characteristic of distributed medium sharing, the scheduling algorithm employed in wireline networks can not be applied directly to the context of wireless LANs. We argue that delay differentiation in wireless LAN can only be achieved through a joint packet scheduling at the network layer and distributed coordination at the media access control (MAC) layer. Therefore, we present a cross‐layer waiting time priority (CWTP) scheduling algorithm. CWTP consists of two tiers: an intra‐node waiting time priority (WTP) scheduler at the network layer and an inter‐node distributed coordination function at the MAC layer. These two tiers coordinateviaa mapping function, which maps the normalized waiting time at the network layer to the backoff time at the MAC layer. Two mapping schemes, namely linear mapping and piecewise linear mapping, are presented and evaluated in this paper. Extensive simulation results show that the CWTP algorithm can effectively achieve proportional delay differentiation in wireless LANs. Copyright © 2004 John Wiley & Sons, Ltd.

When supporting source-specific heterogeneous-receiver multimedia applications, a multicast tree is built among a source and the receivers such that the path from the source to each receiver satisfies the delay and bandwidth constraints. To optimize the network usage, it is desirable to find a multicast tree that minimizes the total bandwidth used while satisfying the different delay and bandwidth requirements of the receivers. For scalability reasons, the desired protocol should require little or minimum storage in the sender and other on-tree routers. Moreover, to allow dynamic member joining or leaving, a receiver-initiated approach is more appropriate. We describe our receiver-initiated QoS multicast protocol that aims at reducing the bandwidth used in building a multicast tree for heterogeneous receivers by actively identifying better sub-optimal paths. Our protocol does not require additional information to be stored in the on-tree routers, and it is able to construct a better sub-optimal tree than existing protocols.

When supporting source-specific heterogeneous-receiver multimedia applications, a multicast tree is built among a source and the receivers such that the path from the source to each receiver satisfies the delay and bandwidth constraints. To optimize the network usage, it is desirable to find a multicast tree that minimizes the total bandwidth used while satisfying the different delay and bandwidth requirements of the receivers. For scalability reason, the desired protocol should require little or minimum storage in the sender and other on-tree routers. Moreover, to allow dynamic member join or leave, a receiver-initiated approach is more appropriate. In this paper, we describe our receiver-initiated QoS multicast protocol that aims at reducing the bandwidth used in building a multicast tree for heterogeneous receivers by actively identifying better sub-optimal paths. Our protocol does not require additional information to be stored in the on-tree routers, and it is able to construct a better sub-optimal tree than existing protocols.

Improving TCP performance has long been the focus of many research efforts in mobile ad hoc networks (MANET). In this paper, we address one aspect of this endeavor: how to properly set TCP's congestion window limit (CWL) to achieve optimal performance. Past research has shown that using a small CWL improves TCP performance in certain scenarios [M. Gerla et al., Feb. 1999], [Z. Fu et al., Apr. 2003], however, no comprehensive study has been given. To this end, we turn the problem of setting TCP's optimal CWL into identifying the bandwidth-delay product (BDP) of a path in MANET. We first show and prove that, independent of the MAC layer protocol being used, the BDP of a path in MANET cannot exceed the round-trip hop-count (RTHC) of the path. We further refine this upper bound based on the IEEE 802.11 MAC layer protocol, and show that in a chain topology, a tighter upper bound exists, which is approximately 1/5 of the RTHC of the path. Based on this tighter bound, we propose an adaptive CWL setting strategy to dynamically adjust TCP's CWL according to the current RTHC of its path. Using ns-2 simulations, we show that our simple strategy improves TCP performance by 8% to 16% in a dynamic MANET environment.

This paper studies the problem of finding optimal routes for premium class traffic in the DiffServ network such that (1) loop-freedom is guaranteed in the entire network under hop-by-hop routing assumption; and (2) the maximum relative congestion among all links is minimized. This problem is called the extended optimal premium routing (eOPR) problem, which is proven to be NP-hard. We use the integer programming method to mathematically formulate the eOPR problem and find the optimal solutions for small scale networks. we also study heuristic algorithms in order to handle large scale networks. Simulation results are compared to handle large scale networks. Simulation results are compared with the optimal solutions obtained by solving the integer programming models. The results show that the bandwidth-inversion shortest path (BSP) algorithm can be a good candidate to route premium traffic in DiffServ networks.

Internet heterogeneity has been a major problem in multimedia data delivery. To deal with the problem, overlay proxy networks as well as distributed and composable services across these overlay networks are being deployed. This solution however, implies that the overlay networks must support not only data multicast for data delivery to a group of destinations, but also service multicast (incorporate services in the distribution tree) for semantic data transformations in order to deal with Internet heterogeneity. This paper presents challenges and solutions fro building service multicast trees. We compare two groups of algorithms, the shortest-service-path tree (SSPT) algorithm and the longest-match (LM) algorithm. Simulation results show trade-offs between complexity and overall tree performance, as well as cost differences when further refinements of the LM approach are considered.

With increasing demand for multimedia and real-time applications, local area network (LAN) technologies are rapidly being upgraded to support quality-of-service (QoS). Many QoS-enabled LANs are making use of resource allocation mechanisms that can discriminate among traffic classes of different priorities. When such LANs are interconnected by bridges to form an extended LAN, it is necessary to upgrade the bridges so that they are QoS-enabled as well. For example, the IEEE 802.1p standard defines a framework for priority queuing in bridges. Alternatively, frame forwarding decisions at the link later may be modified to recognize frame priorities and alternate paths may be used for differentiating QoS. In this paper, we describe a novel bridge protocol that can forward frames of different priorities using different paths. Our protocol ensures that the forwarding path of a higher priority frame is never longer than the forwarding path of a lower priority frame.

In the conventional internal border gateway protocol (IBGO), the BGP sessions between all BGP speakers in a single autonomous system (AS) form a full mesh. For scalability reasons, route reflection is proposed as an alternative to the full mesh inside an AS. The selection of route reflectors and their clients determines the path used by IGBP route advertising. Thus, the design of the route reflection graph is an important issue for improving IBGP operating efficiency and reliability. This paper focuses on the topology optimization for the route reflection graph, i.e., the selection of the reflectors and the interconnections between reflectors and clients. We propose the optimum reflection graph (ORG) problem to find the best topology for IBGP reflection according to the efficiency or reliability metrics. We give the solvability conditions for the problem and present solution based on the integer programming model. Our approach is also highly flexible. Human decisions or constraints can be easily incorporated to find a topology which satisfies AS administrator's manual configurations.

Many value-added and content delivery services are being offered via service level agreements (SLAs). These services can be interconnected to form a service overlay network (SON) over the Internet. Service composition in SON has emerged as a cost-effective approach to quickly creating new services. Previous research has addressed the reliability, adaptability, and compatibility issues for composed services. However little has been done to manage generic quality-of-service (QoS) provisioning for composed services, based on the SLA contracts of individual services. In this paper we present QUEST a QoS assUred composEable Service infrasTructure, to address the problem. QUEST framework provides: (1) initial service composition, which can compose a qualified service path under multiple QoS constraints (e.g., response time, availability). If multiple qualified service paths exist, QUEST chooses the best one according to the load balancing metric; and (2) dynamic service composition, which can dynamically recompose the service path to quickly recover from service outages and QoS violations. Different from the previous work, QUEST can simultaneously achieve QoS assurances and good load balancing in SON.

In traditional computer systems, security is typically provided in a one-or-nothing manner; the system is either secure or insecure. Such an approach is insufficient for pervasive environments that contain heterogenous devices with varying computing resources. The small, portable handheld devices are often left unsecured due to their limited computing power. The approach is also inadequate for multimedia applications that require security as a controllable service attribute to maintain performance quality of service to levels that are acceptable to the users. Hence, we need a tunable and differentiable security framework. In this paper, we present a quality of protection (QoP) framework that resolves the inadequacies of the one-or-nothing approach by providing differential security levels for different device, user and application security requirements and preferences. We show that our QoP framework is necessary for multimedia applications to achieve the best possible security and performance levels in pervasive environments.

Networked video sensors need to execute two dependent periodic tasks: video encoding and transmission. The dependency and periodicity often result in small idle intervals of CPU and wireless network interface card (WNIC). In this paper, we present a sender-buffering approach to exploit such idle intervals for energy saving. Specifically, a video sensor encodes frames in a timely fashion, but buffers encoded frames and transmits them in bursts at longer intervals. In doing so, it (1) accumulates short WNIC idle intervals into longer ones, during which the WNIC can enter the lower-power sleep mode, and (2) slows down the CPU by avoiding CPU idle intervals, which are resulted from both early completion of frame encoding and waiting for frame transmission. Our experimental results show that the buffering approach can save 32-80% CPU energy and 35- 54% WNIC energy, while increasing the overall end-to-end transmission delay by at most 2 frames.

The paper targets the problem of optimal resource allocation in overlay multicast, which poses both theoretical and practical challenges. Theoretically, resource allocation among overlay flows is not subject to the network capacity constraint but also the data constraint, mainly due to the dual role of end hosts as both receivers and senders. Practically, existing distributed resource allocation schemes assume the network links to be capable of measuring flow rates, calculating and communicating price signals, none of which actually exists in the Internet today. We address these challenges as follows. First, we formalize the problem using nonlinear optimization theory, which incorporates both network constraint and data constraint. Based on our theoretical framework, we propose a distributed algorithm, which is proved to converge to the optimal point, where the aggregate utility of all receivers is maximized. Second, we propose an end-host-based solution, which relies on the coordination of end hosts to accomplish tasks originally assigned to network links. our solution can be directly deployed without any changes to the existing network infrastructure.

In the internal border gateway protocol (IBGP), route reflection is widely used as an alternative to full mesh IBGP sessions inside an AS for scalability reason. However, some important issues, such as the impact of route reflection on the reliability of IBGP and the construction of reliable reflection topology with unreliable routers or links, have not been well investigated. This paper addresses the problem of finding reliable route reflection topologies for IBGP networks, which is of great importance to increase the robustness of IBGP operations. We first present a novel reliability model and two new metrics (IBGP expected lifetime and expected session loss) to evaluate the reliability of reflection topologies, and further to investigate the design problem. After studying the solvability conditions under the router capacity constraints, we prove the NP-hardness of the problem, and then design and implement three heuristic solutions using randomization techniques: heuristic selection, greedy search and simulated annealing. Our extensive computational experiments show that the reliability of IBGP reflection network can be significantly improved by our solutions.

Efficient video data management calls for intelligent video summarization tools that automatically generate concise video summaries for fast skimming and browsing. Traditional video summarization techniques are based on low-level feature analysis, which generally fails to capture the semantics of video content. Our vision is that users unintentionally embed their understanding of the video content in their interaction with computers. This valuable knowledge, which is difficult for computers to learn autonomously, can be utilized for video summarization process. In this paper, we present an intelligent video browsing and summarization system that utilizes previous viewers' browsing log to facilitate future viewers. Specifically, a novel ShotRank notion is proposed as a measure of the subjective interestingness and importance of each video shot. A ShotRank computation framework is constructed to seamlessly unify low-level video analysis and user browsing log mining. The resulting ShotRank is used to organize the presentation of video shots and generate video skims. Experimental results from user studies have strongly confirmed that ShotRank indeed represents the subjective notion of interestingness and importance of each video shot, and it significantly improves future viewers' browsing experience.

No abstract available.

In this paper, we propose a peer-to-peer streaming solution to address the on-demand media distribution problem. We identify two issues, namely the asynchrony of user requests and heterogeneity of peer network bandwidth. Our key techniques to address these two issues are cache-and-relay and layer-encoded streaming. A unique challenge of layered peer-to-peer streaming is that the bandwidth and data availability (number of layers received) of each receiving peer are constrained and heterogeneous, which further limits the bandwidth and data availability of its downstream node when it acts as the supplying peer. This challenge distinguishes our work from existing studies on layered multicast. Our experiments show that our solution is efficient at utilizing bandwidth resource of supplying peers, scalable at saving server bandwidth consumption, and optimal at maximizing streaming qualities of all peers.

Pervasive computing allows a user to access an application on heterogeneous devices continuously and consistently. However it is challenging to deliver complex applications on resource-constrained mobile devices, such as cellular telephones and PDA. Different approaches, such as application-based or system-based adaptations, have been proposed to address the problem. However existing solutions often require degrading application fidelity. We believe that this problem can be overcome by dynamically partitioning the application and offloading part of the application execution to a powerful nearby surrogate. This will enable pervasive application delivery to be realized without significant fidelity degradation or expensive application rewriting. Because pervasive computing environments are highly dynamic, the runtime offloading system needs to adapt to both application execution patterns and resource fluctuations. Using the fuzzy control model, we have developed an offloading inference engine to adaptively solve two key decision-making problems during runtime offloading: (1) timely triggering of adaptive offloading, and (2) intelligent selection of an application partitioning policy. Extensive trace-driven evaluations show the effectiveness of the offloading inference engine.

Distributed weighted fair scheduling schemes for QoS support in wireless networks have not yet become standard. In this paper we propose an admission control and dynamic bandwidth management scheme that provides fairness in the absence of distributed link level weighted fair scheduling. In case weighted fair scheduling becomes available, our system assists it by supplying the scheduler with weights and adjusting them dynamically as network and traffic characteristics vary. To obtain these weights, we convert the bandwidth requirement of the application into a channel time requirement. Our bandwidth manager then allots each flow a share of the channel time depending on its requirement relative to the requirements of other flows in the network. It uses a max-min fairness algorithm with minimum guarantees. The flow controls its packet transmission rate so it only occupies the channel for the fraction of time allotted to it by the bandwidth manager. As available bandwidth in the network and the traffic characteristics of various flows change, the channel time proportion allotted also dynamically varies. Our experiments show that, at the cost of a very low overhead, there is a high probability that every flow in the network will receive at least its minimum requested share of the network bandwidth.

We present an advance reservation scheme for CPU and energy resources, called ReCalendar. The goal is to enable soft real-time applications on mobile devices to achieve desired performance and lifetime. ReCalendar achieves this goal via two distinctive phases, calendaring and scheduling. In the calendaring phase, a calendar is used to arrange applications and to request CPU and energy reservations for calendared applications. In the scheduling phase, the resource manager enforces admitted reservations via CPU frequency/voltage adaptation and frequency-aware soft real-time scheduling. We have implemented the ReCalendar prototype and experimentally shown that, compared with previous approaches that support only immediate reservation or CPU advance reservation, ReCalendar achieves longer battery lifetime and higher overall system utility.

This paper presents GRACE-OS, an energy-efficient soft real-time CPU scheduler for mobile devices that primarily run multimedia applications. The major goal of GRACE-OS is to support application quality of service and save energy. To achieve this goal, GRACE-OS integrates dynamic voltage scaling into soft real-time scheduling and decides how fast to execute applications in addition to when and how long to execute them. GRACE-OS makes such scheduling decisions based on the probability distribution of application cycle demands, and obtains the demand distribution via online profiling and estimation. We have implemented GRACE-OS in the Linux kernel and evaluated it on an HP laptop with a variable-speed CPU and multimedia codecs. Our experimental results show that (1) the demand distribution of the studied codecs is stable or changes smoothly. This stability implies that it is feasible to perform stochastic scheduling and voltage scaling with low overhead; (2) GRACE-OS delivers soft performance guarantees by bounding the deadline miss ratio under application-specific requirements; and (3) GRACE-OS reduces CPU idle time and spends more busy time in lower-power speeds. Our measurement indicates that compared to deterministic scheduling and voltage scaling, GRACE-OS saves energy by 7% to 72% while delivering statistical performance guarantees.

The performance of ad hoc routing protocols will significantly degrade, if there are malfunctioned nodes in the network. Fault tolerant routing protocols address this problem by exploring the network redundancy through multipath routing. Designing an effective and efficient fault tolerant routing protocol is inherently hard, because the problem is NP-complete and the precise path information is unavailable. This paper solves this problem by presenting an end-to-end estimation-based fault tolerant routing algorithm E/sup 2/FT. E/sup 2/FT deploys two complementary processes: route estimation and route selection. Through end-to-end performance measurement, the route estimation process gives improving estimation results via iterations. Based on these estimation results, the route selection process decides a multipath route for packet delivery. The route selection is refined progressively with the increasingly accurate estimation result using "confirmation" and "dropping" procedures. Through theoretical analysis and simulation, we show E/sup 2/FT can achieve a high packet delivery rate with acceptable overhead.

Peer-to-peer (P2P) computing grids consist of peer nodes that communicate directly among themselves through wide-area networks and can act as both clients and servers. These systems have drawn much research attention since they promote Internet-scale resource and service sharing without any administration cost or centralized infrastructure support. However aggregating different application services into a high-performance distributed application delivery in such systems is challenging due to the presence of dynamic performance information, arbitrary peer arrivals/departures, and systems' scalability requirement. In this paper we propose a scalable QoS-aware service aggregation model to address the challenges. The model includes two tiers: (1) on-demand service composition tier which is responsible for choosing and composing different application services into a service path satisfying the user's quality requirements; and (2) dynamic peer selection tier, which decides the specific peers where the chosen services are actually instantiated based on the dynamic, composite and distributed performance information. The model is designed and implemented in a fully distributed and self-organizing fashion. Conducting extensive simulations of a large-scale P2P system (10/sup 4/ peers), we show that our proposed model and algorithms achieve better performance than several common heuristic algorithms.

In mobile ad hoc networks, the locations and resource-richness of mobile nodes vary dynamically. The information used for routing by traditional routing protocols becomes obsolete due to node mobility. To overcome this problem, a predictive location-based QoS routing scheme is needed. We present a location-delay prediction scheme, based on a location-resource update protocol, which assists a QoS routing protocol. Simulation results show that our approach can predict the location at a given instant in the future with a high degree of accuracy.

The delay-constrained least-cost routing problem is to find the least cost path which satisfies a given delay constraint. There are two major difficulties to solve in this problem. The first difficulty is the NP-completeness of this routing problem. The second difficulty is that the networking information used for routing may be imprecise. The ticket-based routing (TBR) algorithm, aiming to find a sub-optimal solution, provides a heuristic approach to overcome the above difficulties and solve the routing problem. Although TBR proposes a detailed ticket forwarding method based on imprecise end-to-end information, it does not optimize the ticket probing process so as to find better paths. This paper proposes an enhanced ticket-based routing (ETBR) algorithm. The ETBR improves the effectiveness of ticket probing by two techniques. The first technique uses color-based ticket distribution for tickets of different colors. The tracing information of green tickets and yellow tickets is kept separately to avoid unnecessary ticket dropping. The second technique uses historical probing results to optimize ticket probing, so that redundant probing paths are eliminated. Through extensive simulations, we demonstrate that the ETBR can find paths which have much lower cost than TBR, without decreasing the success ratio or increasing the message overhead.

Ubiquitous computing promotes the proliferation of various stationary, embedded and mobile devices interconnected by heterogeneous networks. It leads to a highly dynamic distributed system with many devices and services coming and going frequently. Many emerging distributed multimedia applications are being deployed in such a computing environment. In order to make the experience for a user truly seamless and to provide soft performance guarantees, we must meet the following challenges: (1) users should be able to perform tasks continuously, despite changes of resources, devices and locations; (2) users should be able to efficiently utilize all accessible resources within runtime environments to receive the best possible Quality-of-Service (QoS). In this paper, we propose an integrated QoS-aware service configuration model to address the above problems. The configuration model includes two tiers: (1) service composition tier, which is responsible for choosing and composing current available service components appropriately and coordinating arbitrary interactions between them to achieve the user's objectives; and (2) service distribution tier which is responsible for dividing an application into several partitions and distributing them to different available devices appropriately. Our initial experimental results based on both prototype and simulations show the soundness of our model and algorithms.

Reducing initial delay of playing time, delay jitter during playing time, and network bandwidth utilization is important for multimedia streaming service. In order to achieve these two goals, our peer-to-peer multimedia caching service exploits the proximity of set of clients and temporal and spatial locality of cached streams on the clients. Peer clients, which are fully connected together, not only receive multimedia streams from a server, but also send cached streams to peer clients like a proxy server upon its request. All information about the peer clients, and video segment they cache, quality of connections, are maintained by a cache service via the publish/subscribe protocols. This paper shows approaches for multimedia streaming, streaming scheduling, pre-fetching and switching streaming clients in the collaborative environment of peer-to-peer clients. Our simulation results show provisioning small initial delay and low jitter during playing time.

Much of the current multimedia application deployment relies on composable service systems, where complex multimedia services are composed dynamically from multiple simpler ones that may be widely distributed. Related to such a scenario is the problem of finding feasible service paths that meet end-to-end requirements. Research has been done in discovering individual (unicast) service paths. In this paper, we apply multicast at the application layer, and develop a multicast service routing protocol that builds service trees whose individual paths satisfy end-to-end service requirements. Our ns-2 simulation results show that, compared to unicast, multicast can save up to 40% in communication cost, 43% in resource usage, yet incur less message overhead.

Many features of traditional TV service are becoming desirable for software multimedia applications to provide open solutions and improve flexibility. We have studied a new kind of service, what we call "visual information embedding", that comes from the idea of picture-in-picture. Based on previous work in MPEG-compressed domain algorithms, we propose a "backtracking" approach that reduces the decoding complexity up to 90% and enables real-time processing on MPEG2 streams at the price of a delay of one group-of-picture period. We have implemented a software real-time visual information embedding gateway for HDTV streams, and the experimental results have shown that our solution is practical and efficient.

To enable the end-to-end quality of service (QoS) guarantees in the Internet, based on the border gateway protocol (BGP), inter-domain QoS advertising and routing are important. However, little research has been done in this area so far. Two major challenges, scalability and heterogeneity, make the QoS extension to BGP difficult. Two existing approaches, link capacity routing (LCR) and available bandwidth routing (ABR), address QoS advertising and routing in BGP with respect to the bandwidth metric, but neither of them can solve the two challenges well. We extend BGP to advertise bandwidth information, but, instead of using link capacities or instantaneous available bandwidth values, a novel QoS metric, available bandwidth index (ABI), is defined and used to perform bandwidth advertising and routing. The two major contributions of ABI are: (1) ABI dynamically abstracts available bandwidth into a probability interval, therefore, it is very flexible to represent heterogenous and dynamic bandwidth values; (2) by capturing the statistical property of the detailed available bandwidth distribution, ABI is so efficient that it can highly decrease the message overhead in routing, thereby making the QoS advertising and routing very scalable. Our extensive simulations confirm both contributions of the ABI extension to BGP very well.

Group communication has become increasingly important in mobile ad hoc networks (MANET). Current multicast routing protocols in MANET have a large overhead due to the dynamic network topology. To overcome this problem, there is a recent shift towards stateless multicast in small groups. We introduce a small group multicast scheme, based on packet encapsulation, which uses a novel packet distribution tree construction algorithms for efficient data delivery. The tree is constructed with the goal of minimizing the overall bandwidth cost of the tree. Two construction algorithms, for a location-guided k-ary (LGK) tree and a location-guided Steiner (LGS) tree, utilize the geometric locations of the destination nodes as heuristics to compute the trees. They are accompanied by a hybrid location update mechanism to disseminate location information among a group of nodes. Our simulation results show that LGS tree has lower bandwidth cost than LGK tree when the location information of the nodes is up-to-date, and its cost is similar to that of an optimal Steiner multicast tree. When location information of the nodes is out-dated, LGK tree outperforms LGS tree due to its lower computational complexity.

For the provision of quality of service (QoS) in the Internet, differentiated service (DiffServ) has been proposed as a cost-effective solution. Traffic is classified into several service classes with different priorities, premium class traffic being the highest. The routing algorithm used by the premium class service has significant effects on the traffic of all other classes as well as its own. Shortest hop-count routing used in the current Internet is no longer sufficient in DiffServ networks. Based on hop-by-hop routing, an optimal routing algorithm must be found for premium class traffic such that (1) it works correctly and efficiently for premium traffic; (2) it reduces negative influences (such as bandwidth starvation, excessive delay jitter, etc.) to other traffic classes. This problem, the optimal premium-class routing (OPR) problem, is NP-complete. To handle the OPR problem, first, we analyze the strength and weaknesses of two existing algorithms (widest-shortest-path algorithm and bandwidth-inversion shortest-path algorithm). Second, we apply to the OPR problem a novel heuristic algorithm, called the enhanced bandwidth-inversion shortest-path (EBSP) algorithm. We prove theoretically the correctness of the EBSP algorithm, i.e., it is a consistent and loop-free hop-by-hop routing algorithm. Our extensive simulations in different network environments show clearly that the EBSP algorithm performs better for premium class traffic in complex, heterogeneous networks than the other two hop-by-hop routing algorithms.

In Internet audio applications, delay and delay jitter affect mostly the applications' quality of service. Since packet delays are different and changing over time, the receiver needs to buffer some amount of packets before playout. Therefore, the amount of buffered packets and the timing of playout are very important for the performance of applications. We adopt an autoregressive (AR) model for estimation of packet delay and deploy a robust identification algorithm for adjustment of the parameters of the AR process. In our preliminary experiments, this robust algorithm leads to better performance when the noise is correlated and/or non-stationary, and also it is robust to model uncertainties.

Ubiquitous computing promises a computing environment that seamlessly and pervasively delivers applications to the user, despite changes of resources, devices, and locations. However, few ubiquitous multimedia applications (UMAs) exist up-to-date. One of the main reasons lies in the fact that it is difficult and error-prone to build a UMA which is mobile and deployable in different ubiquitous environments, and still provides acceptable application-specific Quality-of-Service (QoS) guarantees. In this paper, we present the design and implementation of a novel programming framework, called 'QCompiler" to address the challenges. The framework includes (1) a high-level application specification for the application developer to easily write a UMA with specific quality, mobility, and ubiquity supports, (2) a meta-data compilation, which provides automated consistency checks, translations, and substitutions, to relieve the application developer from dealing with complex programming related to quality, mobility, and ubiquity, (3) a binding, which prepares a quality-aware specification to be executable, in a specific deployment environment, and (4)a run-time meta-data execution, utilizing the meta-data compilation's results, to manage and control a quality-aware multimedia application. As a case study, we apply the programming framework to build a mobile Video-on-Demand (VoD) application. The experimental results show tradeoffs between easiness and flexibility to develop and deploy UMA, and overheads during UMA instantiation and adaptation.

Battery-powered mobile devices are becoming increasingly important computing platforms, which require low energy consumption while meeting the resource demands of a dynamic application workload. Most proposed dynamic voltage scaling (DVS) algorithms, targeting either best-effort or hard real-time systems, however, cannot be directly applied to such open mobile systems. This paper presents a framework to integrate DVS into soft real-time (SRT) scheduling for open mobile systems, achieving energy saving of DVS while preserving resource guarantees of SRT scheduling. The integrated framework makes three major contributions. First, multimedia applications reserve resource based on their average resource usage, without the knowledge of worst-case execution time, which is difficult to estimate in an open mobile environment. Second, the SRT scheduling ensures the correctness of reservation admission and enforcement in a variable speed context. Finally, the DVS manager reduces the processor energy consumption by utilizing the unallocated resource, reclaiming the allocated but unused resource, or avoiding the unused resource. Our extensive simulation results demonstrate that our framework is able to save 4% to 32% energy while slightly affecting application performance.

Global computational grids bring together distributed computation/communication resources. Beyond this, we envision the emergence of global 'service grids', which provide a 'market' of application-level distributed services for clients to discover and to request. We study the issue of wide-area service discovery in service grids. We start with an existing basic wide-area service discovery framework. The framework adopts a scalable architecture consisting of a hierarchy of Discovery Servers. We then identify problems with the basic framework, and propose our enhancement of query responsiveness and QoS awareness. The key techniques we introduce include: (1) the addition of QoS feedback capability to clients; and (2) the caching and propagation of discovery results with QoS feedback in the discovery server hierarchy. With these techniques, the enhanced service discovery framework will be faster in finding qualified service providers. Furthermore, it will select a 'good' (with respect to the QoS to be delivered) service provider for each querying client, based on QoS feedback.

It is challenging to reduce the processor power consumption while meeting the processor resource requirement of distributed multimedia applications in portable computers. This paper presents a middleware framework coordinating the processor/power resource management (PPRM) in a mobile computing environment. Our framework has four major contributions: (1) providing a power-aware resource reservation mechanism, where admission control is based on the processor utilization and power availability; (2) adjusting the speed and corresponding power consumption of the processor depending upon events and triggered by the change of the system workload or power availability; (3) updating reservation contracts of multimedia applications to maintain their resource requirements while adjusting the processor speed; (4) notifying applications about the change of resource status to enable them to adapt their behavior and complete tasks before the power drains. Our experimental results show the effectiveness of the coordinating PPRM framework to save energy and maintain the resource requirements of multimedia applications.

Building and dynamically configuring component-based systems is an important topic in distributed systems and ubiquitous computing. However, the systematic and automatic configuration management remains a challenging problem for the following reasons: (1) QoS-enforced service delivery demands to maximize the system performance out of the best configuration, (2) dynamically varied resource availability in the distributed environment makes it desirable to achieve the optimized system resource consumption. We present a graph-based dependency management model to address the above problems. Our model integrates the management of inter-component functional dependency, including consistency checking and automatic system configuration, as well as QoS-aware resource dependency management. Based on the model, we present a pruning-based configuration selection algorithm, which is able to consistently optimize the system resource consumption, while preserving the QoS level in a heteregeneous environment. Our initial simulation results prove the soundness of our model and algorithm.

With up-coming quality of service (QoS) requirements raised by a wide range of communication-intensive, real-time multimedia applications, resource reservation is one of the approaches to satisfy requested QoS. The Resource ReSerVation Protocol (RSVP) is a well-known signaling protocol for bandwidth reservation in the Internet. However, the current reservation protocols like RSVP do not present the maximum flow acceptance rate solution. The problem of finding the maximum reserved flow acceptance rate and minimizing the effects of crank-back procedure is called the New Killer Reservation Problem (NKR). We study the NKR problem and answer two questions: (1) under what scenarios does the NKR problem become a serious problem; and (2) how can we solve this problem. We introduce the marginable bandwidth reservation protocol (MBR) as a solution to the NKR problem. The results show that MBR protocol yields the desired improvement of the flow acceptance rate at a low cost.

A scalable middleware solution for QoS-aware multimedia service delivery in the ubiquitous computing environment remains a challenging problem. Its heterogeneous and dynamic nature demands a more flexible and intelligent framework. We present SMART, a self-reconfigurable component-based middleware solution to address this issue. The system responds to the reconfiguration requests from applications and users to offer extensibility and efficiency. Furthermore, we present an active controlling mechanism, in which the middleware is able to adapt to the environment variation in a timely fashion. In addition, we describe user management in our framework, which preserves our user-centric philosophy. Finally, we discuss MobiMan, an integrated multimedia service platform as the proof-of-concept application of SMART .

The provision of distributed multimedia services is becoming mobile and ubiquitous. Different multimedia services require application-specific Quality of Service (QoS). In this paper, we present QoSTalk, a unified component-based programming environment that allows application developers to specify different application-specific QoS requirements easily. In QoSTalk, we adopt a hierarchical approach to model application configuration graphs for different distributed multimedia services. We design and implement the XML-based Hierarchical QoS Markup Language, called HQML, to describe the hierarchical configuration graph as well as other application-specific QoS requirements and policies. QoSTalk promotes the separation of concerns in developing QoS-aware ubiquitous multimedia applications and thus enables easy programming of QoS-aware applications, running on top of a unified QoS-aware middleware framework. We have prototyped the QoSTalk in Java and CORBA. Our case studies with several multimedia applications show that QoSTalk effectively fills the gap for application developers between the very general facilities provided by the QoS-aware middleware and different kinds of distributed multimedia applications.

Distributed multimedia applications require end-to-end quality of service(QoS) in order to be accepted and used. One approach to achieve end-to-end QoS is to provide end-to-end resource reservations. Resource ReSerVation Protocol (RSVP) [3] [1] is a unicast and multicast signalling protocol for setting up network bandwidth reservation. In this paper, we propose a solution for securing RSVP messages in a flexible, efficient and scalable manner. Our solution extends the RSVP protocol with a scalable QoS protection, using a hybrid hierarchical security approach. The RSVP messages go through two different protocol treatments - one within subnetworks and the other across subnetworks. We use delayed integrity checking within the subnetwork by sending feedback messages from the egress node. A stronger integrity and encryption check is made on messages sent across subnetworks. Our solution is thus an intermediate approach between the extremes of hop by hop authentication [2] and the SDS/CD protocols [5],and overcomes the drawbacks of the two protocols.

In ad-hoc networks, geographical routing protocols take advantage of location information so that stateless and efficient routing is feasible. However such routing protocols are heavily dependent on the existence of scalable location management services. We present a novel scheme to perform scalable location management. With any location management schemes, a specific node, A, in the network trusts a small subset of nodes, namely its location servers, and periodically updates them with its location. Our approach adopts a similar strategy, but a different and original approach to select such location servers. First, we present a selection algorithm used to designate location servers of a node by its identifier. Second, we propose a hierarchical addressing model for mobile ad-hoc networks, where node locations could be represented at different accuracy levels. With this approach, different location servers may carry location information of different levels of accuracy and only a small set of location servers needs to be updated when the node moves. Through rigorous theoretical analysis, we are able to show that the control message overhead is bounded under our scheme. Finally, simulation results are presented to demonstrate the performance of our location management scheme.

No abstract available.

We consider the problem of designing systems for the transmission of video signals of the quality found in current television broadcasts, over high-speed segments of the public IP network. Our most important contribution is the definition of a network/coder interface for IP networks which gathers channel state information, and then sets parameters of the video coder to maximize the quality of the signal delivered to the receiver, while remaining fair to other data or video connections. This interface plays a role analogous to that of a Leaky Bucket controller, in that it specifies traffic shaping parameters which result in simultaneous good Quality-of-Service (QoS) for the source and good network performance. Since the network is not assumed to provide any form of QoS guarantee, fundamental to our construction is a hidden Markov model for the channel, based on which the interface solves a problem of optimal stochastic control, to decide how to configure the encoder. Other contributions are: a) modifications to the standard Internet transport protocol, to make it suitable for the transport of delay-constrained traffic and to gather channel state information, and b) the design of an error-resilient video coder. Experimental studies reveal that the proposed system is able to stream video signals of the quality of current TV-broadcasts, among hosts in wide-area networks connected to the experimental vBNS backbone.

Large networks are often structured hierarchically by grouping nodes into different domains in order to deal with the scaling problem. The internal topologies of the domains are aggregated before broadcasting and this process is called topology aggregation. We propose a new method of aggregating networks that are delay-bandwidth sensitive. Traditional approaches represent each logical link as a delay-bandwidth pair which is basically a point on a delay-bandwidth plane. We introduce a new QoS parameter representation and present an aggregation algorithm with corresponding routing protocol. Our simulation results show that the algorithm has very good performance in terms of success ratio and crankback ratio.

The first decades of the new millennium will witness an explosive growth in the number and diversity of networked devices and portals. We foresee high degrees of mobility, heterogeneity, and interactions among computing devices connected to global networks. While previous research in distributed operating systems solved many problems related to resource management, they seldom addressed the problems of heterogeneity and dynamic adaptability. On the other hand, middleware solutions, like CORBA and Java/Jini, solve part of the heterogeneity problem by permitting seamless communication among different platforms. But, they do not address dynamic resource management and adaptability for applications requiring high-performance distributed computing. This paper presents 2K, an integrated operating system architecture that addresses the problems of resource management in heterogeneous networks, dynamic adaptability and configuration of component-based distributed applications.

Presents a QoS and contention-aware multi-resource reservation algorithm to provide end-to-end QoS guarantees for distributed and component-based services. We study a reservation-enabled environment, where each type of resource can be reserved. However, the goals of: (1) achieving the best end-to-end QoS for each client, and (2) increasing the overall success rate of resource reservations for different service requests, are in conflict with each other. Our algorithm provides a solution to alleviate this conflict. For each service request, the algorithm computes an end-to-end multi-resource reservation plan which (1) achieves the highest level of end-to-end QoS under the constraint of current resource availability, and (2) tends to incur low bottleneck resource contention among all feasible reservation plans for this service request. Our initial simulation results show the excellent performance of this algorithm.

Widely deployed multimedia services are expected to accommodate clients in a highly heterogeneous environment. Clients of a multimedia service may vary greatly in processing and communication capabilities. In addition, due to workload, location, and service time differences, the availability of end-to-end resources between a client and a server may also vary. Current solutions tend to focus on either the qualitative heterogeneity (in client and resource types) or the quantitative heterogeneity (in resource availability) problem. We present a framework for dynamic end-to-end multimedia service configuration and reservation: an integrated solution to both aspects of the heterogeneity problem. Service configuration is responsible for choosing appropriate service components to compose a customized service deliver to each client; while service reservation is responsible for reserving the end-to-end resources in a coordinated manner and providing the best possible quality within the chosen configuration. We have implemented a prototype of this framework as part of the 2K operating system and tested it by building a proof-of-concept video streaming service on top of it. Our experiments show the soundness of this framework.

The future Internet is expected to support multicast applications with quality of service (QoS) requirements. To facilitate this, QoS multicast routing protocols are pivotal in enabling new receivers to join a multicast group. However, current routing protocols are either too restrictive in their search for a feasible path between a new receiver and the multicast tree, or burden the network with excessive overhead. We propose QMRP, a new Qos-aware multicast routing protocol. QMRP achieves scalability by significantly reducing the communication overhead in constructing a multicast tree, yet it retains a high chance of success. This is achieved by switching between single-path routing and multiple-path routing according to the current network conditions. The high-level design of QMRP makes it operable on top of any unicast routing algorithm both intra-domain and inter-domain. Its responsiveness is improved by using a termination mechanism which detects the failure as well as the success of routing without the use of timeout. In addition, QMRP always constructs loop-free multicast trees.

Large networks are often structured hierarchically by grouping nodes into different domains in order to deal with the scaling problem. In such networks, it is infeasible to maintain the detailed network information at every router. Therefore, the topology information of the domains are summarized before being broadcast. This process is called topology aggregation. Hierarchical routing protocols are then used to find a route among the domains. We study several basic problems associated with hierarchical QoS routing, including (1) how to make QoS-aware topology aggregation, (2) how to represent the aggregated network state, and (3) how to find an end-to-end route based on aggregated information. The novelty in this research is our new network QoS representation which is line segments on the delay-bandwidth plane. We also present a distributed routing mechanism that works with our representation. Our theoretical and simulation results show that the protocol achieves scalability and improved routing performance.

The future Internet is expected to support multicast applications with quality of service (QoS) requirements. To facilitate this, QoS multicast routing protocols are pivotal in enabling new receivers to join a multicast group. However, current routing protocols are either too restrictive in their search for a feasible path between a new receiver and the multicast tree, or burden the network with excessive overhead. We propose QMRP, a new QoS-aware multicast routing protocol. QMRP achieves scalability by significantly reducing the communication overhead of constructing a multicast tree, yet it retains a high chance of success. This is achieved by switching between single-path routing and multiple-path routing according to the current network conditions. The high level design of QMRP makes it operable on top of any unicast routing algorithm in both intradomain and interdomain. Its responsiveness is improved by using a termination mechanism which detects the failure as well as the success of routing without the use of timeout. In addition, QMRP always constructs loop-free multicast trees.

We consider the problem of coding images for transmission over error-prone channels. The impairments we target are transient channel shutdowns, as would occur in a packet network when a packet is lost, or in a wireless system during a deep fade: when data is delivered it is assumed to be error-free, but some of the data may never reach the receiver. The proposed algorithms are based on a combination of multiple description scalar quantizers with techniques successfully applied to the construction of some of the most efficient subband coders. A given image is encoded into multiple independent packets of roughly equal length. When packets are lost, the quality of the approximation computed at the receiver depends only on the number of packets received, but does not depend on exactly which packets are actually received. When compared with previously reported results on the performance of robust image coders based on multiple descriptions, on standard test images, our coders attain similar PSNR values using typically about 50-60% of the bit rate required by these other state-of-the-art coders, while at the same time providing significantly more freedom in the mechanism for allocation of redundancy among descriptions.

With the rapid progress in processor speeds and large network bandwidth availability, it is legitimate to ask the following question: Do we still need quality of service (QoS) mechanisms and policies for shared high-performance distributed computing when timely guarantees are required for their applications? Many researchers believe that through overprovisioning each application will get its QoS without any specific mechanisms and policies. This paper argues that even with overprovisioning we do not achieve always QoS guarantees, especially in the timing domain. By discussing and analyzing currently evolving QoS-aware resource management in a shared computing and communication environment, we show the necessity of QoS provision and its role in high-performance distributed computing environments.

The "wait-for-all" feedback consolidation algorithm avoids consolidation noise, but suffers from a slow transient response. Probabilistic aggregation and fast overload indication techniques have been proposed to preserve the advantages of the "wait-for-all" algorithm and improve its slow transient response. However, these techniques introduce a new "threshold setting" problem. We developed a new consolidation algorithm to provide a fast transient response, a low consolidation noise and flexibility in determining the threshold. In addition, we developed a dynamic timeout mechanism to handle the non-responsive branches where the membership within a multicast session changes due to frequent joining/leaving of members (leaves).

In this paper we present a QoS-directed error control scheme (QDEC) for video multicast in wireless networks. In order to provide multimedia services to mobile users, it is necessary to have not only sufficient bandwidth, but also effective control over wireless channel errors, which are characterized as bursty and location-dependent. The QDEC scheme includes: (1) a video QoS differentiation specification to direct the error control; and (2) three error control algorithms with corresponding channel error conditions under which the algorithms should be chosen. Through analysis and simulation, we show that QDEC has the following key properties: (1) high error recovery rate for QoS-essential video frames, (2) excellent scalability, (3) high transmission efficiency, and (4) no QoS degradation for receivers who observe no channel errors.

The problem of video transmission over IP networks is considered in this paper: we present the design of a complete system for the transmission of high-quality video signals over the public Internet. Our system involves: (1) modifications to the standard Internet transport protocol (TCP), to make it suitable for the transport of delay-constrained traffic, and to gather channel state information; (2) the design of an error-resilient video coder; and (3) the design of a controller which uses channel state information provided by the modified protocol, to configure the error-resilient coder accordingly. Experimental studies performed on certain high-speed segments of the public Internet reveal that the proposed system is able to deliver video signals at bit rates in the range of 4-9 Mbits/sec, not only within fast LANs but also, and more surprisingly, even between sites in the US and Europe. That is, over existing public IP channels, our proposed system can deliver video at TV-broadcast quality.

Distributed multimedia applications have quality-of-service (QoS) requirements specified in terms of constraints on various metrics such as bandwidth and delay. The task of QoS routing is to find a path from the source node to the destination node with sufficient resources to support the required end-to-end QoS. We propose several distributed algorithms for the bandwidth-constrained routing and the delay constrained routing. The algorithms are presented in the form of distributed recursive computation (DRC). DRC computes the global routing state in a distributed, recursive fashion and often leaves useful information at intermediate nodes during the process. An information-reuse scheme is studied to utilize such information in order to reduce the overall overhead. Our simulation shows that the overhead of the proposed algorithms is modest and stable.

In an ad hoc network, all communication is done over wireless media, typically by radio through the air, without the help of wired base stations. Since direct communication is allowed only between adjacent nodes, distant nodes communicate over multiple hops. The quality-of-service (QoS) routing in an ad hoc network is difficult because the network topology may change constantly, and the available state information for routing is inherently imprecise. In this paper, we propose a distributed QoS routing scheme that selects a network path with sufficient resources to satisfy a certain delay (or bandwidth) requirement in a dynamic multihop mobile environment. The proposed algorithms work with imprecise state information. Multiple paths are searched in parallel to find the most qualified one. Fault-tolerance techniques are brought in for the maintenance of the routing paths when the nodes move, join, or leave the network. Our algorithms consider not only the QoS requirement, but also the cost optimality of the routing path to improve the overall network performance. Extensive simulations show that high call admission ratio and low-cost paths are achieved with modest routing overhead. The algorithms can tolerate a high degree of information imprecision.

In heterogeneous environments with performance variations present, multiple applications compete for and share a limited amount of system resources and suffer from variations in resource availability. These complex applications are desired to adapt themselves and to adjust their resource demands dynamically. On one hand, current adaptation mechanisms built within an application cannot preserve global properties such as fairness; on the other hand, adaptive resource management mechanisms built within the operating system are not aware of data semantics in the application. In this paper, we present a novel middleware control framework to enhance the effectiveness of quality-of-service (QoS) adaptation decisions by dynamic control and reconfiguration of internal parameters and functionalities of a distributed multimedia application. Our objective is to satisfy both system-wide properties (such as fairness among concurrent applications) and application-specific requirements (such as preserving the critical performance criteria). The framework is modeled by the task control model and the fuzzy control model, based on rigorous results from the control theory, and verified by the controllability and adaptivity of a distributed visual tracking application. The results show validation of the framework, i.e., critical application quality parameters can be preserved via controlled adaptation.

New emerging distributed multimedia applications provide guaranteed end-to-end quality of service (QoS) and have stringent constraints on delay, delay-jitter, cost, etc. The task of QoS routing is to find a route in the network which has sufficient resources to satisfy the constraints. The delay-cost-constrained routing problem is NP-complete. We propose a heuristic algorithm for this problem. The idea is to first reduce the NP-complete problem to a simpler one which can be solved in polynomial time, and then solve the new problem by either an extended Dijkstra's algorithm or an extended Bellman-Ford algorithm. We prove the correctness of our algorithm by showing that a solution for the simpler problem must also be a solution for the original problem. The performance of the algorithm is studied by both theoretical analysis and simulation.

We develop a QoS-aware traffic shaper, which is implemented in the LRMP (Local Resource Management Protocol) of the VGAnet Suite. In contrast to the conventional traffic shapers, e.g., leaky buckets and token buckets, which simply shape the average rate (and burst) of data transmission without any knowledge of the QoS parameters, e.g., delay jitter and synchronisation skew, the QoS-aware traffic shaper cooperating with the connection management and data transfer protocols provides a service for delivering the continuous-media streams with the bandwidth guarantee, jitter control, and skew control on top of the 100VG-Any LAN.

The goal of quality-of-service (QoS) routing is to find a network path which has sufficient resources to satisfy certain constraints on delay, bandwidth and/or other metrics. The network state information maintained at every node is often imprecise in a dynamic environment because of nonnegligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The information imprecision makes QoS routing difficult. The traditional shortest-path routing algorithm does not provide satisfactory performance with imprecise state information. We propose a distributed routing scheme, called ticket-based probing, which searches multiple paths in parallel for a satisfactory one. The scheme is designed to work with imprecise state information. It allows the dynamic trade-off between the routing performance and the overhead. The state information of intermediate nodes is collectively used to guide the routing messages along the most appropriate paths in order to maximize the success probability. The proposed algorithm consider not only the QoS requirements but also the cost optimality of the routing path. Extensive simulations show that our algorithm achieve high call-admission ratio and low-cost routing paths with modest overhead. The algorithm can tolerate high degree of information imprecision.

We consider the problem of image coding for communication systems that use diversity to overcome channel impairments. We focus on the special case in which there are two channels of equal capacity between a transmitter and a receiver. Our designs are based on a combination of techniques successfully applied to the construction of some of the most efficient wavelet based image coding algorithms, with multiple description scalar quantizers (MDSQs). For a given image, we produce two bitstreams, to be transmitted over each channel. Should one of the channels fail, each individual description guarantees a minimum image quality specified by the user. However, if both descriptions arrive at destination, they are combined to produce a higher quality image than that achievable based on individual descriptions. We formulate a discrete optimization problem, whose solution gives parameters of the proposed encoder yielding optimal performance in an operational sense. Simulation results are presented.

We propose an integrated QoS routing framework based on selective probing for high-speed packet-switching networks. The framework is fully distributed and depends only on the local state maintained at every individual node. By using controlled diffusion computations, the framework captures the common messaging and computational structure of distributed QoS routing, and allows an efficient implementation due to its simplicity. Different distributed routing algorithms (DRAs) can be quickly developed by specifying only a few well-defined constraint-dependent parameters within the framework. Our simulation shows that the overhead of the proposed algorithms is stable and modest.

Variable bit rate (VBR) transmission is widely regarded as the best solution for the transport of compressed image and video data, both in terms of network utilization and quality of data decoded at the receiver. However, significant problems remain unsolved to make this a viable approach. One of these problems is that of efficiently matching characteristics of the VBR source to those of the channel, in order to maximize end-to-end system performance. In this work, we propose a model for the channel based on which the source can make optimal decisions regarding bit allocation and rate control. This model consists of N queues, for each of which the source negotiates with the network statistical performance guarantees, consisting of allowable average transmission and packet loss rates. Based on such a channel description, the source determines how to allocate packets to each queue, to minimize the expected distortion of the images reconstructed at the receiver. A provably optimal algorithm for computing such bit allocations is the core of this work. Simulation results are presented.

to obtain cost-effective QoS.In this paper, we address the issues in designing metrics that are important in evaluating the Quality of Service(QoS) In this paper, we address the issues in designing metrics that are important in evaluating the QoS of video transmission.There has been little work in det ermining effective metrics of QoS for video transmission that characterize both cost (revenue generated or service demand) and guaranteed service.The metrics of analysis and comparison for video transmission must be determined as an end-to-end measure of QoS from video server to end-user(s).By developing these metrics, we hope to enhance the client, server and networking components of a system with monitoring capabilities to measure and evaluate video characterizations.This paper is organized as follows.In Section 2, we discuss a workload model for developing and understanding QoS metrics.Section 3 presents empirical studies and experimental justification for the metric selection based on the three systems -VOSAIC, hierarchical VOD and the remote VCR systems.Section 4 proposes a new integrated metric for measuring video QoS and the analytical framework to express the tradeoffs.We also propose a metric-based QoS architecture along with negotiation and reward protocols.In Section 5 we discuss related work and conclude with future research directions in Section 6. of video transmission.We propose a new metric for video QoS called the weighted cost-satisfaction ratio based on requirements from two perspectives: the user and the service provider.To understand real video workload environments and user behavioral patterns, we obtained and analyzed empirical results from the VOSAIC (video-over-the-Web) system, a hierarchical video-on-demand (VOD) system and a remote VCR system.Based on these results, we define parameters of resource consumption (storage and network bandwidth etc.) and user satisfaction (jitter, syncbronization skew) and derive analytical interrelationships among the metric parameters.We also draw an economic relationship between the user-satisfaction and resource consumption factors to solve metric optimization relations.

This paper describes four scheduling algorithms and two packet-dropping algorithms for transmitting soft-real-time variable-bit-rate traffic in a packet-switched network. Simulation results show that the first-come-first-serve algorithm, which is equivalent to the earliest-deadline-first algorithm when all packets have equal relative deadlines, outperforms the timed-token algorithm as well as two other real-time scheduling algorithms. We also simulated the last-in-first-discard (LIFD) algorithm as well as a more complex (1,k) packet-dropping algorithm which, for each stream, tries to drop no more than I packet in any sequence of k packets. We found that although the (1,k) dropping algorithm does significantly reduce the failure rate, the same reduction in this failure rate can be achieved by slightly lowering the network utilization and using the simpler LIFD algorithm.

The OMEGA architecture provides end-to-end quality-of-service (QoS) guarantees for distributed applications. QoS parameters are translated between application and network requirements by the QoS broker, thus integrating media and network QoS management into a single entity. Admission control uses a schedulability test derived from application requirements. A novel task priority and precedence-based scheme is used to represent complex application requirements and ensure correct feasible schedules. A prototype of OMEGA has been implemented using workstations connected by a 155 Mb/s dedicated ATM local-area network (LAN). To simplify implementation, we assumed networked multimedia application with periodic media streams, specifically a master/slave telerobotics application. This application employs media with highly diverse QoS requirements (e.g., interarrival times, loss rate, and bandwidth) and therefore provides a good platform for testing how closely one can achieve QoS guarantees with workstation hosts and cell-switching. Experience with this implementation has helped to identify new challenges to extending these techniques to a larger domain of applications and systems, and suggests promising new research questions.

Several architectural choices possible in logical multiplexing, accommodating QoS dynamics, and functional divisions between senders and receivers are discussed. The authors' application requirements favor integration rather than channelization, supporting quality of service (QoS) dynamics with a renegotiation mechanism, and pushing complexity towards receivers. The choices are tested via implementation of a telerobotics/teleoperation application. This application is particularly challenging due to its demand for multiple concurrent media streams with varied QoS requirements.