Collaborative Mixed Reality (CollabMR) enables multiple users to interact with both the physical and virtual worlds in real time. One of the major challenges CollabMR faces is the timely synchronization of shared virtual content, yet how network Quality of Service (QoS) maps to user Quality of Experience (QoE) remains poorly understood. To address this gap, we present an exploratory, multi-layer framework linking QoS inputs (latency, bandwidth) to system responsiveness and perceptual, cognitive, and behavioral QoE. We evaluate this framework through a controlled within-subject human study with 60 participants (30 pairs) using HoloLens 2 headsets and a collaborative 3D puzzle task under four network conditions. To validate, we analyze network-level measurements, interaction logs, task performance metrics, and post-task subjective questionnaires to examine how variations in system responsiveness manifest across our conceptual model.

The exponential growth of large-scale AI models has led to computational and power demands that can exceed the capacity of a single data center. This is due to the limited power supplied by regional grids that leads to limited regional computational power. Consequently, distributing training workloads across geographically distributed sites has become essential. However, this approach introduces a significant challenge in the form of communication overhead, creating a fundamental trade-off between the performance gains from accessing greater aggregate power and the performance losses from increased network latency. Although prior work has focused on reducing communication volume or using heuristics for distribution, these methods assume constant homogeneous power supplies and ignore the challenge of heterogeneous power availability between sites.

Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy. Since FL model training may span hundreds of devices and is thus resource- and energy-intensive, it has a significant carbon footprint. Importantly, since energy's carbon-intensity differs substantially (by up to 60×) across locations, training on the same device using the same amount of energy, but at different locations, can incur widely different carbon emissions. While prior work has focused on improving FL's resource- and energy-efficiency by optimizing time-to-accuracy, it implicitly assumes all energy has the same carbon intensity and thus does not optimize carbon efficiency, i.e., work done per unit of carbon emitted.

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.

This paper introduces an alternate approach for encrypting video streams using attribute-based encryption (ABE), focusing on securing the data rather than the connection between streaming endpoints. This fundamental shift in the security model means we can now encrypt video segments once at the source and cache them anywhere reducing the computational load on intermediate caches by removing the need for encryption and decryption on a per-client basis. Additionally, restricting or revoking access can be done by revoking users' private keys rather than re-encrypting the entire video stream. Finally, our approach eliminates the need for decryption and encryption at the intermediate caches (currently required for TLS terminations) since an encrypted version of the content can be stored anywhere, reducing the load on the cache.

This work aims to enable efficient digital rights management (DRM) for volumetric video by introducing attribute-based selective coordinate encryption for point clouds. By encrypting only a subset of coordinates, our approach reduces computational overhead and latency while maintaining necessary security. Selective encryption ensures that point cloud frames—and, by extension, entire volumetric videos—are sufficiently obfuscated so that, while the content remains viewable, it appears highly distorted and visually unpleasant, preventing meaningful unauthorized viewing. We propose a flexible framework that allows varying the amount and type of co-ordinate encryption (e.g., X, Y, Z, or combinations), and we assess visual degradation using established point cloud quality metrics. Our results show that encrypting only X coordinates cuts encryption and decryption times by 37% and 46%, respectively, compared to full-frame encryption, while X and Y encryption achieves 20% and 36% reductions, both still significantly degrading visual quality. Leveraging Attribute-Based Encryption (ABE) further enables content to be securely cached and efficiently distributed in its protected form, eliminating the need for re-encryption, thereby reducing computational load and latency. While our current evaluation is limited to individual point cloud frames, future work will extend to entire volumetric video streams, including analysis of caching gains during streaming with ABE.

Video streaming has evolved from push-based, broad-/multicasting approaches with dedicated hard-/software infrastructures to pull-based unicast schemes utilizing existing Web-based infrastructure to allow for better scalability. In this article, we provide an overview of the foundational principles of HTTP Adaptive Streaming (HAS), from video encoding to end user consumption, while focusing on the key advancements in adaptive bitrate algorithms, Quality of Experience (QoE), and energy efficiency. Furthermore, the article highlights the ongoing challenges of optimizing network infrastructure, minimizing latency, and managing the environmental impact of video streaming. Finally, future directions for HAS, including immersive media streaming and neural network-based video codecs, are discussed, positioning HAS at the forefront of next-generation video delivery technologies.

In the aftermath of the pandemic, governments and organizations worldwide are proactively preparing for future challenges. The importance of communication and connection has been underscored in recent years, especially through applications like Skype, Discord, and Zoom, as they play a pivotal role in collaboration and innovation across the world. Virtual platforms have become integral for societies to connect and collaborate, emphasizing the need for the evolution of communication methods to ensure a resilient global future. This paper provides an implementation for an Extended Reality (XR) conferencing application. This required implementing an XR-capable application for the HoloLens 2 as well as creating a server application that acts as a hub for all connected users, with a focus on simplicity and modality to allow for future modification and experimentation. Once these applications were created, further testing was performed to evaluate network metrics, and user quality of experience through a user study. The user study evaluates the effects of latency on object movement and creates a starting point for further improvement on the created messaging system. The development and deployment of a basic conferencing application for the HoloLens 2 is an important step in building a more connected and resilient future.

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.

Many Internet of Things (IoT) applications require compute resources that cannot be provided by the devices themselves. On the other hand, processing of the data generated by IoT devices and sensors often has to be performed in real- or near real-time, i.e., with stringent latency requirements in constrained environments (e.g., intermittent network connectivity and limited power envelopes). Examples of such scenarios are autonomous vehicles in the form of cars and drones where the processing and analysis of observational data (e.g., video feeds) need to be performed expeditiously to allow for safe operation of the vehicles and to deliver the results in a timely fashion to the stakeholders of the mission. To support the compute and timeliness requirements of such applications, it is essential to include suitable edge resources to process these workflows, and to develop an end-to-end system that can route the vehicles dynamically and process and deliver mission-critical data and analyzed results. In this paper, we develop and evaluate a dynamic scheduling approach that considers complex tradeoffs between real-time constraints, network availability, and latency sensitivity of the mission. We devise an optimized route planning and data transmission schedule for drone flights. The scheduling algorithm is encapsulated in a novel end-to-end architecture (FlyPaw) and an associated adaptive drone mission control system, which enables deployment and management of an integrated cyberphysical system (CPS) – from real drone testbed to base stations to edge-to-cloud resources. The planning algorithm takes into account measured network communication characteristics, estimated uncertainties of future data link connectivity, and data timeliness requirements of the mission to prioritize candidate decision tree solutions based on a risk metric derived from Sharpe's ratio. Our results show that for given task sets, Net Time to Retrieve, our metric describing the time required to perform end-to-end collection and downstream processing of data, can be significantly reduced compared to other naive approaches. The theoretical improvement provided by our algorithm over other naive approaches is dependent on several factors — task locations, network connectivity, processing times and available resources, and is bounded by the duration of the drone flight.

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.

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.

Many organizations maintain and operate large shared computing clusters, since they can substantially reduce computing costs by leveraging statistical multiplexing to amortize it across all users. Importantly, such shared clusters are generally not free to use, but have an internal pricing model that funds their operation. Since employees at many large organizations, especially Universities, have some budgetary autonomy over purchase decisions, internal shared clusters are increasingly competing for users with cloud platforms, which may offer lower costs and better performance. As a result, many organizations are shifting their shared clusters to operate on cloud resources. This paper empirically analyzes the user incentives for shared cloud clusters under two different pricing models using an 8-year job trace from a large shared cluster for a large University system.

In this paper, we describe how we extended the Pegasus Workflow Management System to support edge-to-cloud workflows in an automated fashion. We discuss how Pegasus and HTCondor (its job scheduler) work together to enable this automation. We use HTCondor to form heterogeneous pools of compute resources and Pegasus to plan the workflow onto these resources and manage containers and data movement for executing workflows in hybrid edge-cloud environments. We then show how Pegasus can be used to evaluate the execution of workflows running on edge only, cloud only, and edge-cloud hybrid environments. Using the Chameleon Cloud testbed to set up and configure an edge-cloud environment, we use Pegasus to benchmark the executions of one synthetic workflow and two production workflows: CASA-Wind and the Ocean Observatories Initiative Orcasound workflow, all of which derive their data from edge devices. We present the performance impact on workflow runs of job and data placement strategies employed by Pegasus when configured to run in the above three execution environments. Results show that the synthetic workflow performs best in an edge only environment, while the CASA - Wind and Orcasound workflows see significant improvements in overall makespan when run in a cloud only environment. The results demonstrate that Pegasus can be used to automate edge-to-cloud science workflows and the workflow provenance data collection capabilities of the Pegasus monitoring daemon enable computer scientists to conduct edge-to-cloud research.

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.

Accurate and timely prediction of flash flooding events can be a very useful tool for stormwater officials and first responders. Having lead time with which to issue evacuation directives, to close flood prone roadways, to deploy rescue gear and personnel, and to fortify areas against flooding is essential to minimize property damage and risk of casualties. In this poster, we are presenting a flash flooding prediction workflow based on the Hydrology Lab-Research Distributed Hydrologic Model (HL-RDHM). This workflow leverages cloud computing and the Pegasus Workflow Management System to provide continuous high resolution flood predictions for the Dallas-Fort Worth Metroplex area in North Texas, and can be easily expanded to other regions.

With live video streaming becoming accessible in various applications on all client platforms, it is imperative to create a seamless and efficient distribution system that is flexible enough to choose from multiple Internet architectures best suited for video streaming (live, on-demand, AR). In this paper, we highlight the benefits of such a hybrid system for live video streaming as well as present a detailed analysis with the goal to provide a high quality of experience (QoE) for the viewer. For our hybrid architecture, video streaming is supported simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques to design a flexible system that utilizes the benefits of these varying internet architectures. Also, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, we developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. At the client, the required Internet architecture (TCP/IP versus NDN) can be selected in a transparent and adaptive manner.Based on a prototype we have designed and implemented maintaining efficient use of network resources, we demonstrate that in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Even without caching, our hybrid setup achieves better average bitrate over live video streaming services than its IP-only alternative. Furthermore, we present detailed analysis on ways adaptive video streaming with NDN can be further improved with respect to QoE.

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.

We investigate the rate-distortion (R-D) characteristics of full ultra-high definition (UHD) 360° videos and capture corresponding head movement navigation data of virtual reality (VR) headsets. We use the navigation data to analyze how users explore the 360° look-around panorama for such content and formulate related statistical models. The developed R-D characteristics and modeling capture the spatiotemporal encoding efficiency of the content at multiple scales and can be exploited to enable higher operational efficiency in key use cases. The high quality expectations for next generation immersive media necessitate the understanding of these intrinsic navigation and content characteristics of full UHD 360° videos.

Future drone and air taxi services will take place in the lowest parts of the atmosphere. In the United States, this region is vastly under sampled by existing atmospheric sensing systems and in-situ sensors. As a result, current and future vehicle operators may lack situational awareness of changing weather conditions, bringing uncertainty to aerial ride sharing, cargo delivery and emergency services, impacting up-time, business viability and safety of both vehicles and resources on the ground. This paper presents interviews with drone and air taxi operators on their weather related needs and current sources of weather information as a first step in defining requirements for integrative remote sensing solutions. The solution system is being prototyped and demonstrated in North Texas in collaboration with universities, government agencies and drone operators.

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.

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.

Adverse weather disrupts routine manned aviation operations causing congestion, flight delays and economic losses. However worldwide air travel remains relatively safe and accidents are infrequent due to robust weather observation infrastructure, air traffic management services and regulatory oversight agencies that prioritize a culture of safety. While the introduction and adoption of Unmanned Aircraft Systems is unleashing a new generation of products and services across the world, many unique challenges remain in ensuring safe and efficient flights under unfavorable weather conditions, opening up new areas of research and development. This paper discusses some of these research topics and presents progress being made by the authors in developing end-to-end weather observation and avoidance systems.

No abstract available.

Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. The workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.

Science reproducibility is a cornerstone feature in scientific workflows. In most cases, this has been implemented as a way to exactly reproduce the computational steps taken to reach the final results. While these steps are often completely described, including the input parameters, datasets, and codes, the environment in which these steps are executed is only described at a higher level with endpoints and operating system name and versions. Though this may be sufficient for reproducibility in the short term, systems evolve and are replaced over time, breaking the underlying workflow reproducibility. A natural solution to this problem is containers, as they are well defined, have a lifetime independent of the underlying system, and can be user-controlled so that they can provide custom environments if needed. This paper highlights some unique challenges that may arise when using containers in distributed scientific workflows. Further, this paper explores how the Pegasus Workflow Management System implements container support to address such challenges.

Video streaming is the most popular application on today's Internet. Millions of people around the globe access video contents using various end user devices, such as smart phones, tablets, laptops, and TVs. To meet the requirements of different end user devices and variable network conditions, videos need to be encoded into different quality versions before delivery to the clients. Such large-scale encoding tasks consume significant amounts of energy. In this paper, we investigate to what extent the realtime video encoding clouds can be powered by renewable energy sources. We show that video encoding tasks are suitable for execution on clouds that are powered by a combination of renewable and grid energy sources. With the use of our power management policies, grid energy usage can be reduced by 73-83%, which leads to electricity cost reductions of 14-28% compared to unlimited non-renewable power.

Virtual reality has been gaining popularity in recent years fueled by the proliferation of affordable consumer-grade devices such as Oculus Rift, HTC Vive, and Samsung VR. Amongst the various VR applications, 360° video streaming is currently one of the most popular ones. However, it poses a series of challenges to the serving content distribution systems. One challenge is the significantly increased bandwidth requirement for streaming such content in real time. Recent research has shown that only streaming the content that is in the user's (field-of-view) FoV in high quality can lead to strong bandwidth savings. This can be achieved by analyzing the viewers head orientation and movement based on sensor information. Alternatively, historic information from users that watched the content in the past can be considered to prefetch 360° video data in high quality assuming the viewer will direct the FoV to these areas. This paper presents a 360° video streaming system that transitions between sensor- and content-based predictive mechanisms. We evaluate the effects of our system on the Quality of Experience (QoE) of such a VR streaming system and show that the perceived quality can be increased between 50% and 80% compared to systems that only apply either one of the two approaches.

The rapid growth of mobile devices combined with the vast increase in Internet traffic is posing a challenge to the underlying infrastructure, especially at the wireless edge. Device-to-device communication (D2D) has been recognized as a solution to alleviate the burden on the infrastructure by slowing down the deployment of new base stations. This paper proposes a step towards D2D communication over Information-Centric Networking (ICN) to facilitate content retrieval via a cross-layer assisted forwarding strategy. By choosing ICN, we can circumvent some of the limitations of current MANET routing protocols. We evaluate our proposed scheme on up to 150 nodes in an urban scenario composed of vehicles and pedestrians. Our simulation results showed that our strategy can download up to 13 times more packages than the baseline with lower latency when the cellular network is present.

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.

No abstract available.

Virtualization on top of high-performance servers has enabled the virtualization of network functions like caching, deep packet inspection. Such Network Function Virtualization (NFV) is used to dynamically adapt to changes in network traffic and application popularity. In this paper, we demonstrate how the combination of Software Defined Networking (SDN) and NFV can support the parallel operation of different Internet architectures on top of the same physical hardware. We introduce our architecture for this approach and evaluate it through a live video streaming application in an actual testbed setup, using CloudLab. We use two vastly different protocol stacks, namely TCP/IP and NDN to demonstrate the capability of our approach. The evaluation of our approach shows that it introduces a new level of flexibility when it comes to operation of different Internet architectures on top of the same physical network.

While adaptive bitrate (ABR) streaming has contributed significantly to the reduction of video playout stalling, ABR clients continue to suffer from the variation of bit rate qualities over the duration of a streaming session. Similar to stalling, these variations in bit rate quality have a negative impact on the users' Quality of Experience (QoE). In this paper, we use a trace from a large-scale CDN to show that such quality changes occur in a significant amount of streaming sessions and investigate an ABR video segment retransmission approach to reduce the number of such quality changes. As the new HTTP/2 standard is becoming increasingly popular, we also see an increase in the usage of QUIC as an alternative protocol for the transmission of web traffic including video streaming. Using various network conditions, we conduct a systematic comparison of existing transport layer approaches for HTTP/2 that is best suited for ABR segment retransmissions. Since it is well known that both protocols provide a series of improvements over HTTP/1.1, we perform experiments both in controlled environments and over transcontinental links in the Internet and find that these benefits also "trickle up'' into the application layer when it comes to ABR video streaming where QUIC retransmissions can significantly improve the average quality bitrate while simultaneously minimizing bit rate variations over the duration of a streaming session.

Mobile traffic volume is increasing rapidly, pressuring the underlying infrastructure to quickly increase its capacity. New applications are further exacerbating this problem. Device-to-Device communication has been long recognized as a means to offload traffic from the infrastructure; however, the host-oriented model of the TCP/IP-based Internet poses challenges to this communication pattern. This paper addresses these issues by proposing a scheme that uses a data-centric model to fetch contents from nearby peers while increasing the resiliency of the network in cases of outages and disasters. We collected real data from social media to create a content request pattern and evaluate our approach through the simulation of realistic urban scenarios. Additionally, we analyze the scenario of large crowds in sports venues. Our simulation results show that we can offload traffic from the backhaul network by up to 51.7%, suggesting an advantageous path to support the surge in traffic while keeping complexity and cost for the network operator at manageable levels.

State-of-the-art software-defined wide area networks (SD-WANs) provide the foundation for flexible and highly resilient networking. In this work, we design, implement, and evaluate a novel architecture (denoted as SABR) that leverages the benefits of software-defined networking (SDN) to provide network-assisted adaptive bitrate streaming. With clients retaining full control of their streaming algorithms, we clearly show that by this network assistance, both the clients and the content providers benefit significantly in terms of quality of experience (QoE) and content origin offloading. SABR utilizes information on available bandwidths per link and network cache contents to guide video streaming clients with the goal of improving the viewer’s QoE. In addition, SABR uses SDN capabilities to dynamically program flows to optimize the utilization of content delivery network caches. Backed by our study of SDN-assisted streaming, we discuss the change in the requirements for network-to-player APIs that enables flexible video streaming. We illustrate the difficulty of the problem and the impact of SDN-assisted streaming on QoE metrics using various well-established player algorithms. We evaluate SABR together with state-of-the-art dynamic adaptive streaming over HTTP (DASH) quality adaptation algorithms through a series of experiments performed on a real-world, SDN-enabled testbed network with minimal modifications to an existing DASH client. In addition, we compare the performance of different caching strategies in combination with SABR. Our trace-based measurements show the substantial improvement in cache hit rates and QoE metrics in conjunction with SABR indicating a rich design space for jointly optimized SDN-assisted caching architectures for adaptive bitrate video streaming applications.

As the CASA DFW Urban Testbed has evolved from a small, Doppler radar network to an operational system providing user decision support, multi-sensor data, images thereof, and a real time alerting mechanism, it has become necessary to make use of the Compute Cloud to efficiently process and classify products in a timely and cost effective manner. The Global Environment for Network Innovations (GENI), was selected for this purpose and this manuscript shall describe how compute resources are managed to reduce idle time, how networks are configured dynamically to maximize performance, and how diverse products are accumulated and processed to serve the diverse user base in the DFW metroplex. We find that the GENI cloud offers superior networking, resource acquisition speed than several commodity clouds, and hardware performance on par.

Crowd sensing exploits users' smart devices and human mobility to collect information on a large scale. To realize a crowd sensing campaign, sensing tasks with spatio-temporal requirements are distributed to the devices that can provide the requested information. Typically, the distribution of sensing tasks relies on a centralized communication infrastructure such as cloud servers. However, such an approach will be unsuitable if access to communication infrastructure is restricted, for example in disaster relief scenarios. To fill this gap, we propose a distributed context-aware framework for disseminating sensing tasks, based on the Named Data Networking (NDN) paradigm. By adding context attributes to Interest packets, we allow a device to utilize this information to make forwarding decisions autonomously, thus guiding the sensing tasks towards the suitable sensing devices. Through intensive evaluation, we show that our framework achieves a timely delivery of sensing tasks, while keeping the communication overhead to a minimum compared to pure geo forwarding and flooding approaches.

The current body of research on Dynamic Adaptive Streaming over HTTP (DASH) contributes various adaptation algorithms aiming to optimize performance metrics such as the Quality of Experience. Intuitively, the heterogeneity of the streaming environment and the underlying technologies lead many of the developed approaches to possess clear performance affinities denoted here as sweet spots. We observe, however, that systematic comparisons of these algorithms are usually conducted within homogeneous player environments.

State-of-the-art Software Defined Wide Area Networks (SD-WANs) provide the foundation for flexible and highly resilient networking. In this work we design, implement and evaluate a novel architecture (denoted SABR) that leverages the benefits of SDN to provide network assisted Adaptive Bitrate Streaming. With clients retaining full control of their streaming algorithms we clearly show that by this network assistance, both the clients and the content providers benefit significantly in terms of QoE and content origin offloading. SABR utilizes information on available bandwidths per link and network cache contents to guide video streaming clients with the goal of improving the viewer's QoE. In addition, SABR uses SDN capabilities to dynamically program flows to optimize the utilization of CDN caches.; [email protected] by our study of SDN assisted streaming we discuss the change in the requirements for network-to-player APIs that enables flexible video streaming. We illustrate the difficulty of the problem and the impact of SDN-assisted streaming on QoE metrics using various well established player algorithms. We evaluate SABR together with state-of-the-art DASH quality adaptation algorithms through a series of experiments performed on a real-world, SDN-enabled testbed network with minimal modifications to an existing DASH client. Our measurements show the substantial improvement in cache hitrates in conjunction with SABR indicating a rich design space for jointly optimized SDN-assisted caching architectures for video streaming applications.

Despite known QoE shortcomings, Dynamic Adaptive Streaming over HTTP (DASH) has been tied with TCP for many years now. The advent of HTTP/2 powered by transport protocols such as QUIC provides an excellent opportunity to revisit adaptive bitrate streaming with respect to QoE. QUIC promises improved congestion control, zero-RTT connection establishment and multiplexing logical streams. In this work, we adapt state-of-the-art DASH players with buffer-based and hybrid (rate/buffer-based) quality adaptation logic to use QUIC. Our main focus lies in contrasting the QoE performance of DASH algorithms running on top of QUIC versus TCP in various environments. Interestingly, we find through testbed and Internet measurements that QUIC does not provide a boost to current DASH algorithms but instead a degradation in the chosen quality bitrates.

In this paper, we present an NDN-based architecture for the dissemination of roadside information to cars. Our focus is on flooding events which are often caused by severe weather which also impacts the communication infrastructure. We compare the performance of different message forwarding strategies in VANETs (Vehicular Adhoc Networks) through simulation. In addition, we analyze how our approach compares to an infrastructure-based approach that is less resilient in the case of natural disaster. Our simulations show that the strategy that considers geographical location and velocity of the vehicle performs better compared to others, which in turn outperforms the infrastructure-based approach.

The dynamics of the application-layer-based control loop of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection for DASH a difficult problem. In this work, we provide a DASH quality adaptation algorithm, named SQUAD, that is specifically tailored to provide a high quality of experience (QoE). We review and provide new insights into the challenges for DASH rate estimation. We found that in addition to the ON-OFF behavior of DASH clients, there exists a discrepancy in the timescales that form the basis of the rate estimates across (i) different video segments and (ii) the rate control loops of DASH and Transmission Control Protocol (TCP). With these observations in mind, we design SQUAD aiming to maximize the average quality bitrate while minimizing the quality variations. We test our implementation of SQUAD together with a number of different quality adaptation algorithms under various conditions in the Global Environment for Networking Innovation testbed, as well as, in a series of measurements over the public Internet. Through a measurement study, we show that by sacrificing little to nothing in average quality bitrate, SQUAD can provide significantlygt ; better QoE in terms of quality switching and magnitude. In addition, we show that retransmission of higher-quality segments that were originally received in low-quality is feasible and improves the QoE.

This paper studies information dissemination in wireless ad hoc networks, using standard routing protocols, such as OLSR, as well as Information-Centric Networking. We performed simulations using NS-3 and ndnSIM with different node counts and transport protocols. Our simulations show that TCP performs better in lower hop count scenarios, while NDN performs better in higher hop count scenarios.

In this work we outline a framework for measurement-based performance evaluation in SDN environments. The SDN paradigm, which is based on a strict separation of the network logic from the underlying physical substrate, necessitates a comprehensive global view of the network state. To augment the network representation, we propose mechanisms for extracting traffic characteristics from network observations which are used to derive performance metrics. Such metrics can be exploited by SDN applications to optimize the performance of SDN services. Given the bursty nature of network traffic and the well known adverse impact of this property on network performance, we propose an approach for extracting flow autocorrelations from switch counters. Our main contribution is a random sampling approach that reduces the monitoring overhead while enabling a fine grained characterization of the flow autocorrelation structure. We analytically evaluate the impact of random sampling and demonstrate how services may use the estimated traffic properties to compute useful performance metrics.

The application-layer based control loops of dynamic adaptive streaming over HTTP (DASH) make video bitrate selection a complex problem. In this work, we review and present new insights into the challenges of DASH rate adaptation. We identify several critical issues that contribute to the degradation of DASH performance with respect to the rate control loops of DASH and TCP. We then introduce a novel DASH quality adaptation algorithm SQUAD, which is specifically designed to ensure high quality of experience (QoE). We implement and test our algorithm together with a number of state-of-the-art quality adaptation algorithms. Through extensive experiments on both testbed and cross-Atlantic Internet scenarios, we show that by sacrificing little to none in average quality bitrate, SQUAD provides significantly better QoE in terms of number and magnitude of quality switches.

No abstract available.

The current approach to transcoding in adaptive bit rate streaming is to transcode all videos in all possible bit rates which wastes transcoding resources and storage space, since a large fraction of the transcoded video segments are never watched by users. To reduce transcoding work, we propose several online transcoding policies that transcode video segments in a "just-in-time" fashion such that a segment is transcoded only to those bit rates that are actually requested by the user. However, a reduction in the transcoding work should not come at the expense of a significant reduction in the quality of experience of the users. To establish the feasibility of online transcoding, we first show that the bit rate of the next video segment requested by a user can be predicted ahead of time with an accuracy of 99.7% using a Markov prediction model. This allows our online algorithms to complete transcoding the required segment ahead of when it is needed by the user, thus reducing the possibility of freezes in the video playback. To derive our results, we collect and analyze a large amount of request traces from one of the world's largest video CDNs consisting of over 200 thousand unique users watching 5 million videos over a period of three days. The main conclusion of our work is that online transcoding schemes can reduce transcoding resources by over 95% without a major impact on the users' quality of experience.

In this article, we take advantage of the user behavior of requesting videos from the top of the related list provided by YouTube to improve the performance of YouTube caches. We recommend that local caches reorder the related lists associated with YouTube videos, presenting the cached content above noncached content. We argue that the likelihood that viewers select content from the top of the related list is higher than selection from the bottom, and pushing contents already in the cache to the top of the related list would increase the likelihood of choosing cached content. To verify that the position on the list really is the selection criterion more dominant than the content itself, we conduct a user study with 40 YouTube-using volunteers who were presented with random related lists in their everyday YouTube use. After confirming our assumption, we analyze the benefits of our approach by an investigation that is based on two traces collected from a university campus. Our analysis shows that the proposed reordering approach for related lists would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. This increase in hit rate would lead to reduction in server load and backend bandwidth usage, which in turn reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.

As shown in recent studies, video streaming is by far the biggest category of backbone Internet traffic in the US. As a measure to reduce the cost of highly over-provisioned physical infrastructures while remaining the quality of video services, many streaming service providers started to use cloud services where physical resources can be dynamically allocated based on current demand. This paper characterizes the performance of Dynamic Adaptive Streaming over HTTP (DASH), a new MPEG standard on adaptive streaming, in the cloud. We seek to answer the following questions that are critical to content providers that are hosting video in clouds: Which data center is the best to host videos? Does geographical distance matter? What type of instance is best suitable depending on different needs? How to efficiently solve the trade-off between performance and cost? The measurement methods and results presented in this paper can be easily expanded into other VoD services, and they allow us to i) characterize DASH behavior when streaming from the cloud; ii) identify the key factors that influence the DASH performance; and iii) suggest improvements for related services.

Large-scale wireless mesh networks, like the ones used as cellular back-haul, operate under circumstances, where individual links are affected by weather conditions. Reliability requirements in wireless mesh networks necessitate the ability to reconfigure the network in the face of changing environmental conditions. In this paper, we present a predictive routing protocol for wireless mesh networks, which operate at millimeter-wave bands with directional links, that uses in-network parameter prediction to make the network adaptive, as opposed to using meteorological weather information from external sources, such as weather radars. We validate our approach through simulations based on real-world weather events, observed through a network of weather radars, and comparisons with approaches that do not make use of predictions but may use the link quality as a parameter in routing decision making. Our results show that our link quality-based predictive approach can achieve throughput performance that is almost 8% better than a link quality-based routing algorithm that does not use prediction for the real weather scenario we use for our simulations.

Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard which adaptively streams video based on the link bandwidth between server and client. DASH encoded videos are chunked in small segments and each segment can have different representations. Switching between these representations enables adaptive streaming, which has the potential to reduce bandwidth consumption in cases where a video is not completely watched. In this paper, we present an analysis on the advantages and disadvantages of using DASH as YouTube's video streaming format. To perform this analysis, we make use of a YouTube video trace and analyze the potential reduction in bandwidth consumption by employing DASH in YouTube, based on user watching patterns. Results from our analysis show that by employing DASH with a segment interval of 2 seconds, we can obtain 95% reduction in bandwidth for low quality videos and up to 83% reduction for HD videos in cases where users do not watch videos completely, which is the case for ~ 42% of all video requests in our trace. Considering all videos requested in the trace the overall bandwidth reduction is 40% for low quality videos and 35% for HD videos.

In this paper, we take advantage of the user behavior of requesting videos from the related list provided by YouTube and the user behavior of requesting videos from the top of this related list to improve the performance of YouTube's caches. We recommend a related list reordering approach which modifies the order of the videos shown on the related list based on the content in the cache. The main goal of our reordering approach is to push the contents already in the cache to the top of the related list and push non-cached contents towards the bottom, which increases the likelihood that the already cached content will be chosen by the viewer. We analyze the benefits of our approach by an investigation that is based on two traces collected from an university campus. Our analysis shows that the proposed reordering approach for related list would lead to a 2 to 5 times increase in cache hit rate compared to an approach without reordering the related list. The increase in hit rate would lead to a 5.12% to 18.19% reduction in server load or back-end bandwidth usage. This increase in hit rate and reduction in back-end bandwidth reduces the latency in streaming the video requested by the viewer and has the potential to improve the overall performance of YouTube's content distribution system. An analysis of YouTube's recommendation system reveals that related lists are created from a small pool of videos, which increases the potential for caching content from related lists and reordering based on the content in the cache.

The growth of smartphones combined with advances in mobile networking have revolutionized the way people consume multimedia data. In particular, users in developing countries primarily rely on smartphones since they often do not have access to more powerful (and more expensive) computing devices. Unfortunately, cellular networks in developing countries have historically had low reliability, due to grid instability and lack of infrastructure. The situation has led network operators to experiment with running cellular towers "off the grid" using intermittent renewable energy sources. In parallel, network operators are also experimenting with co-locating server caches close to cell towers to reduce access latency and back-haul bandwidth. In this paper, we study techniques for optimizing multimedia caches for intermittent renewable energy sources. Specifically, we examine how to apply a blinking abstraction proposed in prior work, which rapidly transitions servers between an active and inactive state, to improve the performance of a multimedia cache powered by renewables, called GreenCache. Our results show that GreenCache's staggered load-proportional blinking policy, which coordinates when servers are active over brief intervals, results in 3X less buffering (or pause) time by the client compared to an activation blinking policy, which simply activates and deactivates servers over long periods as power fluctuates, for realistic power variations from renewable energy sources.

Following advice from the YouTube recommendation system is one of the ways users browse through the videos offered by YouTube. The system presents related videos based on several factors depending on the current video requested. This related videos list can be used by caching infrastructure to reduce network bandwidth consumption. In this paper, we analyze the differences between user-specific recommendation lists. We perform this analysis on 100s of user nodes from all around the world divided into 4 geographical regions using PlanetLab. Based on our analysis, we find that the related videos differ less in the top half (1-10) of the related video list offered by YouTube compared to the bottom half (11-20). Based on our analysis, we suggest that, caching or prefetching of the Top 10 of the related videos is advantageous over a period of time than caching the whole list offered by YouTube.

Compute cloud platforms pay-as-you-use model suits applications which require resources sporadically. Severe weather detection and prediction is one such application. Since severe weather events are rare, dedicating servers for such application wastes resources. In this paper, we present the feasibility of using commercial cloud services for severe weather detection and prediction. We show that commercial cloud services provide the required network capability to perform the real-time operation of weather detection and prediction from the radars to the cloud service instance. We automate the process of weather prediction on the cloud based on the results of our weather detection algorithms.

Since today's weather forecasts only cover large regions every few hours, their use in severe weather is limited. In this paper, we present CloudCast, an application that provides short-term weather forecasts depending on users current location. Since severe weather is rare, CloudCast leverages pay-as-you-go cloud platforms to eliminate dedicated computing infrastructure. CloudCast has two components: 1) an architecture linking weather radars to cloud resources, and 2) a Nowcasting algorithm for generating accurate short-term weather forecasts. We study CloudCast's design space, which requires significant data staging to the cloud. Our results indicate that serial transfers achieve tolerable throughput, while parallel transfers represent a bottleneck for real-time mobile Nowcasting. We also analyze forecast accuracy and show high accuracy for ten minutes in the future. Finally, we execute CloudCast live using an on-campus radar, and show that it delivers a 15-minute Nowcast to a mobile client in less than 2 minutes after data sampling started.

Dedicating high-end servers for executing scientific applications that run intermittently, such as severe weather detection or generalized weather forecasting, wastes resources. While the Infrastructure-as-a-Service (IaaS) model used by today's cloud platforms is well-suited for the bursty computational demands of these applications, it is unclear if the network capabilities of today's cloud platforms are sufficient. In this paper, we analyze the networking capabilities of multiple commercial (Amazon's EC2 and Rackspace) and research (GENICloud and ExoGENI cloud) platforms in the context of a Nowcasting application, a forecasting algorithm for highly accurate, near-term, e.g., 5-20 minutes, weather predictions. The application has both computational and network requirements. While it executes rarely, whenever severe weather approaches, it benefits from an IaaS model; However, since its results are time-critical, enough bandwidth must be available to transmit radar data to cloud platforms before it becomes stale. We conduct network capacity measurements between radar sites and cloud platforms throughout the country. Our results indicate that ExoGENI cloud performs the best for both serial and parallel data transfer with an average throughput of 110.22 Mbps and 17.2 Mbps, respectively. We also found that the cloud services perform better in the distributed data transfer case, where a subset of nodes transmit data in parallel to a cloud instance. Ultimately, we conclude that commercial and research clouds are capable of providing sufficient bandwidth for our real-time Nowcasting application.

User experience is a very important aspect of user generated video streaming service such as YouTube. In this paper, we perform a global study of user experience for YouTube videos using PlanetLab nodes from all over the world. We analyze the number of pauses, accumulative pause time, rate at which videos were downloaded to clients, and how the YouTube infrastructure impact the viewer's experience. The data for this analysis was generated by an automated tool from the traces captured during the PlanetLab-based measurement. Results from this analysis show that on average there are about 2.5 pauses per video. Interestingly, we found that on average 25% of the videos with pauses have an accumulative pause time greater than 15 seconds. This shows that not only the number of pauses but also the total length of pauses has to be analyzed to investigate the user experience of watching videos offered by YouTube. In addition, our results show that there is an inverse relationship between the download rates of the videos and the number of pauses encountered.

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by showing that video playbacks of videos of YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

Steerable sensors, such as pan-tilt-zoom video cameras, expose programmable actuators to applications, which steer them in different directions based on their goals. Despite being expensive to deploy and maintain, existing steerable sensor networks allow only a single application to control them due to the slow speed of their mechanical actuators. To address the problem, we design MultiSense to enable fine-grained multiplexing by (i) exposing a virtual sensor to each application and (ii) optimizing the time to context-switch between virtual sensors and satisfy requests.

Today's cloud computing platforms have seen much success in running compute-bound applications with time-varying or one-time needs. In this position paper, we will argue that the cloud paradigm is also well suited for handling data-intensive applications, characterized by the processing and storage of data produced by high-bandwidth sensors or streaming applications. The data rates and the processing demands vary over time for many such applications, making the on-demand cloud paradigm a good match for their needs. However, today's cloud platforms need to evolve to meet the storage, communication, and processing demands of data-intensive applications. We present an ongoing GENI project to connect high-bandwidth radar sensor networks with computational and storage resources in the cloud and use this example to highlight the opportunities and challenges in designing end-to-end data-intensive cloud systems.

Recent measurement studies have analyzed WLAN performance by means of wireless sniffers that passively capture transmitted frames. Also, for relatively large (enterprise) WLAN scenarios, previous work has investigated multi-sniffer deployments with devices placed far apart in order to capture all traffic in the network (even frames transmitted simultaneously by different nodes at non-interfering locations). However, for both these single- and multi-sniffer scenarios, little attention has been given to the fidelity of an individual device, i.e., the ability of a given sniffer to capture all frames that could have been captured by a more faithful device. We assess this fidelity (a term we make precise in this paper) by running controlled experiments inside an anechoic chamber and analyzing the similarities and differences between the trace file from the device under study and those of additional "shadow" devices placed in its close proximity. Our results show that fidelity varies significantly across sniffers, both quantitatively and qualitatively, and that performance may also depend on the nature of the experiment under study and on slight changes of the sniffer position.

Sensor networks are prone to congestion due to bursty and high-bandwidth data traffic, combined with wireless links and many-to-one data routing to a sink. Delayed and dropped packets then degrade the performance of the sensing application. In this paper, we investigate the value of separate handling of sensor control and data traffic, during times of congestion, in a closed-loop sensor network. We first show that prioritizing sensor control traffic over data traffic decreases the round-trip control-loop delay, and consequently increases the quantity and quality of the data collected by the sensor network. We then ground our analysis in a closed-loop meteorological sensor network, focusing on a storm-tracking application running over a network of X-band radars. Our application measures reflectivity (a measure of the number of scatterers in a unit volume of atmosphere known as a voxel) and tracks storms (i.e., regions of high reflectivity) using a Kalman filter. Considering data quantity, we show that prioritizing sensor control traffic increases the number of voxels, V, that can be scanned given a constant number of reflectivity samples, Nc, obtained per voxel. Here, utility increases linearly with the number of scanned voxels. Considering data quality, we show that prioritizing sensor control traffic increases the number of reflectivity samples, N, that can be obtained per voxel given a constant number of voxels, Vc, to scan. Here, since sensing accuracy improves only as a function of radicN, the gain in accuracy for the reflectivity estimate per voxel as N increases is relatively small except when prioritizing sensor control increases N significantly (such as when sensor control packets suffer severe delays). Because accuracy also degrades as a function of radicN, however, and because prioritizing sensor control traffic reduces the number of control packets dropped, data degradation is mitigated. Considering the performance of the tracking application, we then show that during times of severe congestion, not prioritizing sensor control can actually lead to tracking errors accumulating over time.

A dense weather radar network is an emerging concept advanced by the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). A major goal of CASA is to develop an entirely new paradigm, referred to as Distributed Collaborative Adaptive Sensing (DCAS), for improving the coverage of the lowest portion of the atmosphere through coordinated scanning of low-power, short-range, networked radars. The CASA enterprise designs, develops, and deploys system-level test beds to integrate underlying scientific and technical advances and demonstrate the potential to observe, understand, predict and respond to hazardous atmospheric phenomena-with end users involved from the outset. The first one of DCAS test-beds was deployed in south-west Oklahoma, named as Integrated Project 1 (IP1). It is an end-to-end system of a network of four, low-power, short-range, dual polarization, Doppler radar units, aimed at severe weather and hazardous wind sensing. In this paper, a number of aspects in developing a DCAS radar network for these specific applications are reviewed.

Distributed networks of short-range radars offer the potential to observe winds and rainfall at high spatial resolution in volumes of the troposphere that are unobserved by today's longrange weather radars. One class of potential distributed radar network designs includes Off-the-Grid (OTG) weather radar networks. These are short-range radar nodes designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. Independence of the wired infrastructure (power or communications) would allow OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. This paper will present an OTG network testbed being deployed in Western Massachusetts to support experimentation with OTG nodes. This testbed will focus on the energy performance of the OTG network and the virtualization of the radar sensor.

IP1 is a prototype CASA radar sensor network located in southwestern Oklahoma whose goal is to detect severe weather in the lower part of the atmosphere. At the center of this system's control loop is its Meteorological Command and Control (MC&C). In this paper, we presented the overall control architecture for the IP1 network and highlight new features that have recently been added to the MC&C. We also present an analysis of the MC&C performance based on measurement data from a 5-day operation period. In addition, we introduce a distributed version of the MC&C.

Many sensor network studies assume that the energy cost for sensing is negligible compared with the cost of communications or computing. Opportunities exist to deploy sensor networks utilizing active sensors with a high energy cost such as radar. For a node utilizing radar as its primary sensor, the actual sensing procedure is the main power consumer. In the worst case almost 50% of the power is consumed by the sensing procedure, while only 3% is used for communication, the remainder is consumed by the computing platform. In this paper we examine a wireless sensor network composed of short range radars used to monitor rainfall. These short-range radar nodes are designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. We refer to these networks as "off-the-grid" (OTG) weather radar networks. Independence of the wired infrastructure (power or communications) allows OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. We present a simulation based investigation of such an OTG sensor network. We focus on power management and energy harvesting for the network. We use these simulations to demonstrate how geographic location, battery capacity, optimization of power consumption, and node density have an impact on the performance and operational lifetime of such a sensor network. In addition to these simulations, we present the design and implementation of an OTG prototype sensor node. Experiences and data gained from the operation of this node are used as input parameters for the simulations.

Distributed networks of short-range radars offer the potential to observe winds and rainfall at high spatial resolution in volumes of the troposphere that are unobserved by today's long-range weather radars. One class of potential distributed radar network designs includes Off-the-Grid (OTG) weather radar networks. These are short-range radar nodes designed to be deployed as part of an ad-hoc network and to limit their reliance on existing infrastructure. Independence of the wired infrastructure (power or communications) would allow OTG networks to be deployed in specific regions where sensing needs are greatest, such as mountain valleys prone to flash-flooding, geographic regions where the infrastructure is susceptible to failure, and underdeveloped regions lacking urban infrastructure. This paper will present a system model and simulation framework for the design of OTG networks. The model estimates the energy requirements of the three major system functions, sensing, communicating and computing, as well as power generated from the solar panel. The simulation will be used to develop an energy cost function to be used in control decisions.

In this paper, we focus on a network of rich sensors that are geographically distributed and argue that the design of such networks poses very different challenges from traditional mote-class sensor network design. We identify the need to handle the diverse requirements of multiple users to be a major design challenge, and propose a utility-driven approach to maximize data sharing across users while judiciously using limited network and computational resources. Our utility-driven architecture addresses three key challenges for such rich multi-user sensor networks: how to define utility functions for networks with data sharing among end-users, how to compress and prioritize data transmissions according to its importance to end-users, and how to gracefully degrade end-user utility in the presence of bandwidth fluctuations. We instantiate this architecture in the context of geographically distributed wireless radar sensor networks for weather, and present results from an implementation of our system on a multi-hop wireless mesh network that uses real radar data with real end-user applications. Our results demonstrate that our progressive compression and transmission approach achieves an order of magnitude improvement in application utility over existing utility-agnostic non-progressive approaches, while also scaling better with the number of nodes in the network.

The emerging of rich sensor networks poses very different design challenges from traditional "mote-class" sensor networks. One important challenge is that these networks are designed to handle the diverse requirements of multiple users. In this work, we demonstrate how multiple end user needs are handled in rich sensor networks using a utility-driven architecture. We instantiate this architecture in the context of geographically distributed wireless radar sensor networks for weather, and demonstrate the real-time operation of the prototype on a radar testbed in Okalahoma.

In many energy-rechargeable wireless sensor networks, sensor nodes must both sense data from the environment, and cooperatively forward sensed data to data sinks. Both data sensing and data forwarding (including data transmission and reception) consume energy at sensor nodes. We present a distributed algorithm for optimal joint allocation of energy between sensing and communication at each node to maximize overall system utility (i.e., the aggregate amount of information received at the data sinks). We consider this problem in the context of wireless sensor networks with directional, non-steerable antennas. We first formulate a joint data-sensing and data-routing optimization problem with both per-node energy-expenditure constraints, and traditional flow routing/conservation constraints. We then simplify this problem by converting it to an equivalent routing problem, and present a distributed gradient-based algorithm that iteratively adjusts the per-node amount of energy allocated between sensing and communication to reach the system-wide optimum. We prove that our algorithm converges to the maximum system utility. We quantitatively demonstrate the energy balance achieved by this algorithm in a network of small, energy-constrained X-band radars, connected via point- to-point 802.11 links with non-steerable directional antennas.

Combining the concepts of caching and transmission control protocol (TCP)-friendly streaming of layer-encoded video bears the problem that those videos might not be cached in full quality. Therefore, we focus in this work on the scheduling of retransmissions of missing segments of a cached video in a manner that allows clients to receive the content in an improved quality. In a first step, we conducted subjective assessments of variations in layer-encoded video with the goal to validate existing quality metrics, including our own, which are based on certain assumptions. A statistical analysis of the subjective assessment validates these assumptions. We also show that the frequently used peak signal-to-noise ratio (PSNR) is not an appropriate metric for variations in layer-encoded video. With the insight from the subjective assessment we develop heuristics for retransmission scheduling and prove their applicability by conducting a series of simulations.

The paper presents an investigation on video streaming over a wireless link. The main goal of this work is to investigate how feedback, rate control and stream adaptation can be used to improve the quality of the streamed video. We focus on the recently standardized TCP-friendly rate control (TFRC) protocol as a mechanism for feedback and rate control. Dynamic switching between video streams is used for stream adaptation. Based on simulations, we investigate the challenging scenario of handovers in wireless networks. The results of these simulations show that our approach increases the quality of video stream delivered to a mobile client in a wireless network.

In contrast to classical assumptions in video on demand (VoD) research, the main requirements for VoD in the Internet are adaptiveness, support of heterogeneity, and last but not least high scalability. Hierarchically layered video encoding is particularly well suited to deal with adaptiveness and heterogeneity support for video streaming. A distributed caching architecture is the key to a scalable VoD solution in the Internet. Thus, the combination of caching and layered video streaming is promising for an Internet VoD system, yet, requires thoughts about some new issues and challenges. In this paper, we investigate one particular of these issues: how to deal with retransmissions of missing segments for a cached layered video in order to meet user demands to watch high quality video with relatively few quality variations. We devise a suite of fairly simple retransmission scheduling algorithms and compare these against existing ones by simulative experiments.

Quality of service guarantees for multimedia communication systems have been considered on several abstraction levels. In the multimedia networking field it is typical to identify the minimal QoS requirements of an application to save resources by guaranteeing its functionality. Many of these applications can operate in spite of an imperfect delivery of media data, while other applications such as distributed databases or distributed file systems consider perfect QoS necessary but accept delay. The basic problems of the latter is the consistency of their data, while the former require a consistent perception of the content. More generically, both QoS requirements can be interpreted as a problem of maintaining a consistent system state. Consequently we assume that many distributed applications, including most distributed multimedia applications, can fulfil their tasks in spite of imperfect consistency. Since the application requirements differ widely, the elements that make up "consistency" must be separated and classified. This paper introduces Consistency QoS and proposes a classification of elements that determine an application's consistency requirements. The low level QoS requirements that these separate parameters rely on are shown, and example parameter sets for application classes are given.

Replicating data and services at multiple networked computers increases the service availability of distributed systems. This paper presents the design and implementation architecture of a replication mechanism for a distributed multimedia system medianode which is developed as an infrastructure to share multimedia-enhanced teaching materials among lecture groups. With the replication mechanism, medianode provides enhanced access to presentation materials in both connected and disconnected operation modes. The main contribution of this paper is the identification of new replication requirements in distributed media systems and a multicast-based update propagation mechanism by which not only the update events are signaled, but also the updated data are exchanged between replication managers.

In contrast to audio which is often streamed as complete music titles or even as a life feed from a radio station, video in today's Internet is almost only available as small clips and pre-generated programs. Although some of the problems concerning AV streaming are reasonably solved right now, some work in fields like wide area distribution systems need further investigation to make applications like "true video-on-demand" work. Our research and the one of many others is focused on problems that have to be solved to make application like VoD work in the Internet. It is mainly concerned with wide area distribution. We present a platform for experimental VoD research which is thought to support researchers working on VoD and wide area distribution for audio and video content. This platform offers researchers the possibility to implement their ideas without building a complete streaming environment and in addition allows the combination of different implementations. After motivating the development of our platform we present the design of our platform, give an overview of the actual implementation and the existing components that we have already built. Finally, example scenarios for the use of our platform in research are given.

We think that web caches will soon have to better support multimedia demands.In this paper we present a cache server design for internet video on demand (VoD) systems.1.1