Cross-disciplinary teams increasingly work with high-dimensional scientific datasets, yet fragmented toolchains and limited support for shared exploration hinder collaboration. Prior immersive visualization & analytics research has emphasized individual interaction, leaving open how multi-user collaboration can be supported at scale. To fill this critical gap, we conduct semi-structured interviews with 20 domain experts from diverse academic, government, and industry backgrounds. Using deductive–inductive hybrid thematic analysis, we identify four collaboration-focused themes: workflow challenges, adoption perceptions, prospective features, and anticipated usability and ethical risks. These findings show how current ecosystems disrupt coordination and shared understanding, while highlighting opportunities for effective multi-user engagement. Our study contributes empirical insights into collaboration practices for high-dimensional scientific data visualization & analysis, offering design implications to enhance coordination, mutual awareness, and equitable participation in next-generation collaborative immersive platforms. These contributions point toward future environments enabling distributed, cross-device teamwork on high-dimensional scientific data.

Immersive telepresence has the potential to transform real-time communication through highly interactive and engaging experiences. Despite recent advances in reducing communication and computation costs, existing systems largely overlook packet loss, which can severely degrade the quality of experience (QoE). Recovering lost immersive content is considerably more challenging than in 2D video due to the complexity of dense 3D representations. Recovery must be both accurate and timely while minimizing the communication and computation overhead it incurs. To address these challenges, we present LITE, the first loss-resilient immersive telepresence system. LITE incorporates three key design principles: (1) leveraging semantic communication to transmit compact motion and audio semantics, which can be reconstructed into the remote user's immersive representation and voice, enabling fast semantic-level recovery and remaining robust to congestion-control-induced rate reductions under loss; (2) fusing audio and motion semantics via a lightweight multimodal model to achieve accurate, real-time recovery of motion semantics; and (3) encoding audio semantics from multiple past frames into succinct neural redundancy to enable robust recovery. We prototype LITE using a well-known parametric facial motion representation and extensively evaluate its performance across diverse networks. Our results demonstrate that LITE improves QoE by up to 109% compared with existing schemes, while sustaining real-time streaming at 30 frames per second and preserving high visual fidelity (structural similarity index measure above 0.9, where 1 indicates perfect similarity).

The rise of generative artificial intelligence (GenAI), powered by large language models, has led to the emergence of real-time, voice-based conversational applications that enable dynamic, multi-modal interactions for everyday tasks such as checking the weather or planning a trip. These human-to-GenAI calling applications blend speech processing, generative intelligence, and real-time communication, presenting new challenges in latency optimization, network infrastructure design, and resilience under load. Despite their growing popularity, little is known about the operational characteristics and performance of these applications. This paper conducts an empirical measurement of six human-to-GenAI calling applications from Google, Meta, Microsoft, and OpenAI, focusing on their input/output modalities, network behavior, latency metrics, and robustness. Our findings reveal key design choices and performance bottlenecks in these emerging applications. For example, the conversational latency often reaches several seconds, far exceeding the typical sub-second delays of human-to-human voice communication and potentially impairing interactivity. Moreover, voice-based GenAI traffic is inherently asymmetric: the uplink, carrying real-time human speech, benefits from streaming-based transmission, while the typically large downlink GenAI responses are better served through batch-based delivery.

With the rising demand for cost-effective and privacy-preserving deep learning and visualization services, service providers are increasingly turning to in-browser solutions. General-purpose computations, leveraging the graphics processing unit (GPU), are foundational to executing algorithms that power these services. Web graphics library (WebGL) is a widely adopted GPU-access application programming interface (API) designed for multidimensional rendering in browsers, while WebGPU is a newer API developed with compute-specific capabilities. Although WebGPU is a promising standard, its performance has not been systematically evaluated for general-purpose computation. This paper investigates WebGPU and WebGL for accelerating client-side computation in web browsers. By benchmarking key computational GPU kernels for 16 PolyBench and 2 CHStone functions, we measure the performance of WebGPU and WebGL across varying input sizes and algorithmic complexities. Our results show that: 1) both WebGPU and WebGL exhibit poorer performance than central processing unit (CPU)-based execution for small input data due to setup and CPU-GPU synchronization overheads, but they outperform CPU execution as the input data size increases; 2) WebGL performs better than WebGPU for small inputs, except for CPU-driven loop functions, due to its lower initial setup overhead; and 3) WebGPU outperforms WebGL for large inputs through optimized GPU thread utilization and achieves better performance for loop-driven algorithms across all input sizes by minimizing CPU-GPU data exchange. Overall, our results indicate that WebGPU is a competitive option for enhancing the execution performance of large-scale web applications.

Web 3.0 is redefining the current Web (Web 2.0) with a focus on data and governance decentralization. The InterPlanetary File System (IPFS) exemplifies this shift. However, it faces a trade-off between decentralization and performance: prior studies have shown IPFS's performance degradations but fail to diagnose root causes or deliver actionable fixes.

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.

Image-based mapping and localization offer six degrees of freedom (6DoF) pose estimation for immersive applications. This is achieved by matching, on a server, 2D visual features extracted from a mobile device's camera view and 3D features stored in a map. While effective, this process may lead to privacy breaches (e.g., exposure of sensitive information captured by camera views). To tackle this crucial issue, we present PIPE, a first-of-its-kind Privacy-preserving Image-based 6DoF Pose Estimation system. The design of PIPE is motivated by our key observation that uploading only a small amount of features extracted from camera views for pose estimation could reduce privacy leakage. However, trade-offs exist between privacy preservation, system utility (i.e., pose estimation accuracy), and system performance (e.g., end-to-end latency). To balance the trade-offs, PIPE deliberately explores the feature-detection space to reduce computation latency, designs an efficient feature ranking method by judiciously utilizing map data, and optimizes feature selection by jointly considering the features' ranking and spatial distribution to improve pose estimation accuracy. Moreover, we construct a learning-based metric to quantify the extent of privacy leakage in images. Our extensive performance evaluation reveals that PIPE can effectively preserve privacy and reduce end-to-end latency by up to 22.6%, while marginally affecting pose estimation accuracy (e.g., as low as 2.7%).

We present NIER, a video conferencing system that can adaptively maintain a low bitrate (e.g., 10–100 Kbps) with reasonable visual quality while being robust to packet losses. We use key-point-based deep image animation (DIA) as a key building block and address a series of networking and system challenges to make NIER practical. Our evaluations show that NIER significantly outperforms the baseline solutions.

The InterPlanetary File System (IPFS) is a pioneering effort for Web 3.0, well-known for its decentralized infrastructure. However, some recent studies have shown that IPFS exhibits a high degree of centralization and has integrated centralized components for improved performance. While this change contradicts the core decentralized ethos of IPFS and introduces risks of hurting the data replication level and thus availability, it also opens some opportunities for better data management and cost savings through deduplication.

Social virtual reality (VR) has the potential to replace conventional online social media by offering quasi-real-world social experiences. As such, it has been extensively examined by the research community. However, existing studies fall short of providing a comprehensive understanding of how different aspects of social VR platforms interact to affect user experience. Motivated by this limitation, we conduct a user study with Oculus Quest 2 headsets and dissect the user experience on five social VR platforms. We evenly and randomly divide 42 participants into short-term (spending 10-30 minutes/platform) and long-term (spending at least 120 minutes/platform) groups. Besides employing surveys and interviews, we measure the frame rate and resolution of these platforms and explore how various factors interplay to influence the user experience of social VR. Our findings reveal that the frame rate, resolution, and interactive events of social VR platforms have a more significant impact on the experience of long-term users compared to short-term users. The scalability limitations of these platforms, as evidenced by decreased frame rates with the increasing number of concurrent users, result in an increased prevalence of motion sickness among long-term users, negatively impacting their overall experience. Moreover, the absence of highly interactive events also deteriorates their overall experience, and the low resolution combined with the lack of interactive events further decreases their sense of social presence. Additionally, our study demonstrates several common limitations negatively affecting the experience of both long-term and short-term users. For example, the harassment prevention mechanisms on all five platforms are inadequate, and being harassed has a detrimental effect on users' overall experience and sense of social presence. The avatar embodiment of investigated platforms has limited contribution to users' sense of social presence, mainly due to the lack of realism and full-body tracking. Our findings call for more research in scalability support, motion sickness relief, interactive event design, harassment prevention, and avatar development for improving social VR platforms in the future.

Virtual reality (VR) offers promise in education given its immersive and socially engaging nature, but it can pose challenges for educators when creating VR-specific content. VR videos can function as a new educational tool for VR content creation due to their creation affordability and user-friendliness. However, little empirical research exists on how educators utilize VR videos and associated pedagogy in real classes. Our research employed a contextual inquiry, through in-person interviews and online surveys with 11 instructors to gain actionable insights from envisioned teaching scenarios for VR videos that are informed by actual instructional practices. Our study aims to understand the factors that motivate instructors’ adoption of VR videos, identify challenges educators face when incorporating VR videos into instructional units, and examine pedagogical adjustments when integrating VR videos into teaching. Through empirical evidence, we provide design implications for the development of VR-based learning experiences across diverse educational contexts. Our study also serves as a practical case of how VR can be adopted and integrated into education.

Due to the widespread adoption of "work-from-home" policies, videoconferencing applications (e.g., Zoom) have become indispensable for remote communication. However, they often lack immersiveness, leading to "Zoom fatigue" and degrading communication efficiency. The recent debut of Apple Vision Pro, a mobile headset that supports "spatial personas", offers an immersive telepresence experience. In this paper, we conduct a first-of-its-kind in-depth and empirical study to analyze the performance of immersive telepresence with FaceTime, Webex, Teams, and Zoom on Vision Pro. We find that only FaceTime provides a truly immersive experience with spatial personas, whereas others still operate 2D personas. Our measurements reveal that (1) FaceTime delivers semantic data to optimize bandwidth consumption, which is even lower than that of 2D personas for other applications, and (2) it employs visibility-aware optimizations to reduce rendering overhead. However, the scalability of FaceTime remains limited, with a simple server-allocation strategy that potentially leads to high network delay for users.

Volumetric videos offer a unique interactive experience and have the potential to enhance social virtual reality and telepresence. Streaming volumetric videos to multiple users remains a challenge due to its tremendous requirements of network and computation resources. In this paper, we develop MuV2, an edge-assisted multi-user mobile volumetric video streaming system to support important use cases such as tens of students simultaneously consuming volumetric content in a classroom. MuV2 achieves high scalability and good streaming quality through three orthogonal designs: hybridizing direct streaming of 3D volumetric content with remote rendering, dynamically sharing edge-transcoded views across users, and multiplexing encoding tasks of multiple transcoding sessions into a limited number of hardware encoders on the edge. MuV2 then integrates the three designs into a holistic optimization framework. We fully implement MuV2 and experimentally demonstrate that MuV2 can deliver high-quality volumetric videos to over 30 concurrent untethered mobile devices with a single WiFi access point and a commodity edge server.

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.

Minimizing bandwidth consumption while maintaining satisfactory visual quality becomes the holy grail of volumetric content delivery. However, due to the huge amount of 3D data to stream, the stringent latency requirement, and the high computational workload, achieving this ambitious goal could be challenging for mobile mixed reality headsets, which can naturally enable viewers' motion with six degrees of freedom but have limited computing power. Motivated by our critical observations from a user study of eye movements with 50+ participants, in this paper, we present Theia, a first-of-its-kind gaze-driven and perception-aware volumetric content delivery system that effectively incorporates the following innovations into a holistic system: (1) real-time creation of foveated volumetric content to reduce network data usage; (2) efficient augmentation of foveal content to boost user experience; and (3) adaptive omission of peripheral content for further bandwidth savings based on eye movements. We implement a prototype of Theia using Microsoft HoloLens 2 headsets and extensively evaluate its performance. Our results reveal that compared to the state-of-the-art, Theia can drastically reduce bandwidth consumption by up to 67.0% and enhance visual quality by up to 92.5%.

The increasing popularity of virtual reality (VR) has stressed the importance of authenticating VR users while preserving their privacy. Behavioral biometrics, owing to their robustness and ease of collection, compared to traditional modes such as passwords, have become a favored authentication choice. While current approaches that utilize behavioral biometrics to train classifiers for authentication yield promising accuracy, they cause privacy breaches by sharing sensitive data with a server to train a central model. In this paper, we present MetaFL, a first-of-its-kind privacy-preserving VR authentication framework that leverages federated learning (FL) on multi-modal motion data. The design of MetaFL is motivated by our key insight that various modalities of motion data uniquely affect authentication performance for individual users and among different users. It is attributed to the fundamental challenge of privacy-preserving user authentication: users can access only their own data with limited global knowledge. To tackle this issue, MetaFL judiciously selects the most suitable modalities for each user, which is decomposed into within-user ordering and between-user selection to eliminate the complex interplay between various conflicting factors. Moreover, we develop a personalized strategy to initialize FL models, further improving authentication accuracy. Our extensive performance evaluation on six public datasets shows that MetaFL outperforms state-of-the-art FL-based models (e.g., 17--28% higher authentication accuracy), and its accuracy gap with the non-privacy-preserving central model is small (i.e., only <2%).

Immersive telepresence has the potential to revolutionize remote communication by offering a highly interactive and engaging user experience. However, state-of-the-art exchanges large volumes of 3D content to achieve satisfactory visual quality, resulting in substantial Internet bandwidth consumption. To tackle this challenge, we introduce MagicStream, a first-of-its-kind semantic-driven immersive telepresence system that effectively extracts and delivers compact semantic details of captured 3D representation of users, instead of traditional bit-by-bit communication of raw content. To minimize bandwidth consumption while maintaining low end-to-end latency and high visual quality, MagicStream incorporates the following key innovations: (1) efficient extraction of user's skin/cloth color and motion semantics based on lighting characteristics and body keypoints, respectively; (2) novel, real-time human body reconstruction from motion semantics; and (3) on-the-fly neural rendering of users' immersive representation with color semantics. We implement a prototype of MagicStream and extensively evaluate its performance through both controlled experiments and user trials. Our results show that, compared to existing schemes, MagicStream can drastically reduce Internet bandwidth usage by up to 1195X while maintaining good visual quality.

Thanks to recent advances in immersive technologies, virtual reality (VR) is becoming increasingly popular in online education, particularly in light of the rise of the Metaverse. However, there is currently no in-depth investigation of the user experience of VR-based online education and the comparison of it with video-conferencing-based counterparts. To fill these critical gaps, we conduct multiple sessions of two courses in a university with 10 and 37 participants on Mozilla Hubs (Hubs for short), a social VR platform that is deemed as one of the early prototypes of the Metaverse, and let them compare the classroom experience on Hubs with Zoom, a popular video-conferencing application. In addition to employing traditional analytical methods to understand user experience, we benefit from an end-to-end measurement study of Hubs to corroborate our findings and systematically detect its performance bottlenecks. Our study leads to the following key observations. First, the scalability issue of Hubs makes it inadequate for accommodating large courses. Second, compared to Zoom, Hubs can offer a better sense of place presence and social presence to students, thanks to its avatar-based interactions and the hand and head tracking enabled by headsets. Third, even though VR headsets help students concentrate in class, effectively utilizing learning tools through them remains a challenge.

Social Virtual Reality (VR) is regarded as one of the most popular VR applications since it transcends geographical barriers, allowing users to interact in simulated environments for various purposes. Despite its promising prospects, there is a growing concern about the harassment issue due to the immersive nature of social VR compared to other online social environments. Existing protections against harassment in social VR are highly limited in terms of practical effectiveness. The deficiency of studies toward understanding and preventing harassment in social VR further complicates the regulation and intervention efforts of social VR platforms in such situations. To address these challenges, we, in this paper, quantitatively investigate human interaction behaviors in social VR. More specifically, we first build a customized platform based on Mozilla Hubs, a popular social VR platform, to collect data about users’ social interaction behaviors involving harassment instances. A subsequent analysis of the collected dataset SAHARA (Social interAction beHAviors in vR with hArassment) reveals that the task of online harassment detection in social VR is complicated since it depends on not only users’ actions but also their spatial and temporal relationships. To accurately discern harassment, we propose a novel framework HardenVR (HA-Rassment DEtectioN framework for social VR). As a context-aware harassment detection framework, HardenVR employs a transformer-based model to capture relative poses and learn users’ hand actions in 6-DOF (Degree-of-Freedom). Meanwhile, multiple mechanisms, including the extra attention mechanism, distance-aware clustering method, and the sliding window, have been introduced into the model to handle challenges of data imbalance, over-fitting, and continuous detection. The design of HardenVR aims to achieve the balance between accuracy, efficiency, and cost-effectiveness for the task of harassment detection. As a starting point, HardenVR successfully learns pose information as the context to identify harassment and the experiment results show its detection accuracy as high as 98.26%.

Virtual Reality (VR) has a noteworthy educational potential by providing immersive and collaborative environments. As an alternative but cost-effective way of delivering realistic environments in VR, using 360-degree videos in immersive VR (VR videos) received more attention. Although many studies reported positive learning experiences with VR videos, little is known about how collaborative learning performs on VR video viewing systems. In this study, we implemented two collaborative VR video viewing modes based on the way of group video control, synchronized or shared (Sync mode) and non-synchronized or individual (Non-sync mode) video control, against a conventional VR video viewing setting (Basic mode). We conducted a within-subject study (N = 54) in a lab-simulated remote learning environment. Our results show that collaborative VR video modes (Sync and Non-sync mode) improve users’ learning experiences and collaboration quality, especially with shared video control. Our findings provide directions for designing and employing collaborative VR video tools in online learning environments.

Achieving the optimal balance of minimizing bandwidth consumption and end-to-end latency while preserving a satisfactory level of visual quality becomes the ultimate goal of live, interactive holographic communication, a fundamental building block of immersive telepresence envisioned for 6G. Nevertheless, achieving this ambitious goal poses significant challenges for mobile devices with limited computing power, considering the substantial amount of 3D data to stream, the demanding latency requirements, and the high computation workload involved. Instead of distributing immersive content bit by bit, in this position paper, we propose to deliver semantic information extracted from telepresence participants to drastically reduce Internet bandwidth usage for task-oriented applications such as remote collaboration. We contribute a taxonomy by categorizing related semantics into three different types (i.e., keypoints, 2D images, and text), pinpoint the open research challenges associated with developing a practical system for each category in our comprehensive research agenda, and delve into the potential solutions for overcoming these challenges. The preliminary results from our proof-of-concept implementation that harnesses keypoint-based semantics (partially) validate the feasibility of our research agenda.

By combining various emerging technologies, mobile extended reality (XR) blends the real world with virtual content to create a spectrum of immersive experiences. Although Web-based XR can offer attractive features such as better accessibility, cross-platform compatibility, and instant updates, its performance may not be on par with its standalone counterpart. As a low-level bytecode, WebAssembly has the potential to drastically accelerate Web-based XR by enabling near-native execution speed. However, little has been known about how well Web-based XR performs with WebAssembly acceleration. To bridge this crucial gap, we conduct a first-of-its-kind systematic and empirical study to analyze the performance of Web-based XR expedited by WebAssembly on four diverse platforms with five different browsers. Our measurement results reveal that although WebAssemlby can accelerate different XR tasks in various contexts, there remains a substantial performance disparity between Web-based and standalone XR. We hope our findings can foster the realization of an immersive Web that is accessible to a wider audience with various emerging technologies.

While recent work explored streaming volumetric content on-demand, there is little effort on live volumetric video streaming that bears the potential of bringing more exciting applications than its on-demand counterpart. To fill this critical gap, in this paper, we propose MetaStream, which is, to the best of our knowledge, the first practical live volumetric content capture, creation, delivery, and rendering system for immersive applications such as virtual, augmented, and mixed reality. To address the key challenge of the stringent latency requirement for processing and streaming a huge amount of 3D data, MetaStream integrates several innovations into a holistic system, including dynamic camera calibration, edge-assisted object segmentation, cross-camera redundant point removal, and foveated volumetric content rendering. We implement a prototype of MetaStream using commodity devices and extensively evaluate its performance. Our results demonstrate that MetaStream achieves low-latency live volumetric video streaming at close to 30 frames per second on WiFi networks. Compared to state-of-the-art systems, MetaStream reduces end-to-end latency by up to 31.7% while improving visual quality by up to 12.5%.

VR has received increased attention as an educational tool and many argue it is destined to influence educational practices, especially with the emergence of the Metaverse. Most prior research on educational VR reports on applications or systems designed for specified educational or training objectives. However, it is also crucial to understand current practices and attitudes across disciplines, having a holistic view to extend the body of knowledge in terms of VR adoption in an authentic setting. Taking a higher-level perception of people in different roles, we conducted a qualitative analysis based on 23 interviews with major stakeholders and a series of participatory design workshops with instructors and students. We identified the stakeholders who need to be considered for using VR in higher education, and highlighted the challenges and opportunities critical for VR current and potential practices in the university classroom. Finally, we discussed the design implications based on our findings. This study contributes a detailed description of current perceptions and considerations from a multi-stakeholder perspective, providing new empirical insights for designing novel VR and HCI technologies in higher education.

Social virtual reality (VR) has the potential to gradually replace traditional online social media, thanks to recent advances in consumer-grade VR devices and VR technology itself. As the vital foundation for building the Metaverse, social VR has been extensively examined by the computer graphics and HCI communities. However, there has been little systematic study dissecting the network performance of social VR, other than hype in the industry. To fill this critical gap, we conduct an in-depth measurement study of five popular social VR platforms: AltspaceVR, Horizon Worlds, Mozilla Hubs, Rec Room, and VRChat. Our experimental results reveal that all these platforms are still in their early stage and face fundamental technical challenges to realize the grand vision of Metaverse. For example, their throughput, end-to-end latency, and on-device computation resource utilization increase almost linearly with the number of users, leading to potential scalability issues. We identify the platform servers' direct forwarding of avatar data for embodying users without further processing as the main reason for the poor scalability and discuss potential solutions to address this problem. Moreover, while the visual quality of the current avatar embodiment is low and fails to provide a truly immersive experience, improving the avatar embodiment will consume more network bandwidth and further increase computation overhead and latency, making the scalability issues even more pressing.

The emerging volumetric videos offer a fully immersive, six degrees of freedom (6DoF) viewing experience, at the cost of extremely high bandwidth demand. In this paper, we design, implement, and evaluate Vues, an edge-assisted transcoding system that delivers high-quality volumetric videos with low bandwidth requirement, low decoding overhead, and high quality of experience (QoE) on mobile devices. Through an IRB-approved user study, we build a first-of-its-kind QoE model to quantify the impact of various factors introduced by transcoding volumetric content into 2D videos. Motivated by the key observations from this user study, Vues employs a novel multiview approach with the overarching goal of boosting QoE. The Vues edge server adaptively transcodes a volumetric video frame into multiple 2D views with the help of a few lightweight machine learning models and strategically balances the extra bandwidth consumption of additional views and the improved QoE, indicated by our QoE model. The client selects the view that optimizes the QoE among the delivered candidates for display. Comprehensive evaluations using a prototype implementation indicate that Vues dramatically outperforms existing approaches. On average, it improves the QoE by 35% (up to 85%), compared to single-view transcoding schemes, and reduces the bandwidth consumption by 95%, compared to the state-of-the-art that directly streams volumetric videos.

Mobile headsets should be capable of understanding 3D physical environments to offer a truly immersive experience for augmented/mixed reality (AR/MR). However, their small form-factor and limited computation resources make it extremely challenging to execute in real-time 3D vision algorithms, which are known to be more compute-intensive than their 2D counterparts. In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets. Motivated by our analysis and evaluation of state-of-the-art 3D object detection models, DeepMix intelligently combines edge-assisted 2D object detection and novel, on-device 3D bounding box estimations that leverage depth data captured by headsets. This leads to low end-to-end latency and significantly boosts detection accuracy in mobile scenarios. A unique feature of DeepMix is that it fully exploits the mobility of headsets to fine-tune detection results and boost detection accuracy. To the best of our knowledge, DeepMix is the first 3D object detection that achieves 30 FPS (i.e., an end-to-end latency much lower than the 100 ms stringent requirement of interactive AR/MR). We implement a prototype of DeepMix on Microsoft HoloLens and evaluate its performance via both extensive controlled experiments and a user study with 30+ participants. DeepMix not only improves detection accuracy by 9.1--37.3% but also reduces end-to-end latency by 2.68--9.15×, compared to the baseline that uses existing 3D object detection models.

Mapping and localization are the key components in mobile spatial computing to facilitate interactions between users and the digital model of the physical world. To enable localization, mobile devices keep capturing images of the real-world surroundings and uploading them to a server with spatial maps for localization. This leads to privacy concerns on the potential leakage of sensitive information in both spatial maps and localization images (e.g., when used in confidential industrial settings or our homes). Motivated by the above issues, we present a holistic research agenda in this paper for designing principled approaches to preserve privacy in spatial mapping and localization. We introduce our ongoing research, including learning-assisted noise generation to shield spatial maps, distributed architecture with intelligent aggregation to protect localization images, and end-to-end privacy preservation with fully homomorphic encryption. We also discuss the technical challenges, our preliminary results, and open research problems in those areas.

Multi-user volumetric content delivery can enable numerous appealing applications, such as online education, telehealth, multiuser AR/VR training, immersive collaborative analytics, etc. However, the bandwidth-intensive nature of volumetric video streaming makes existing systems for single-user experiences hard to scale to multi-user scenarios. To address this critical issue, in this paper, we first perform a scaling experiment on mmWave networks that offer the needed multi-Gbps throughput and identify two key challenges of streaming high-quality volumetric videos to multiple users: frequent blockages of mmWave links and high transmission redundancy among users. To solve these problems, we propose a first-of-its-kind, agile, and cross-layer system, dubbed M5, for improving the performance and quality of experience for multi-user volumetric video streaming. M5 utilizes the 6DoF motion prediction of users to proactively adapt mmWave beams and prefetch frames to mitigate the blockage effects. Furthermore, it takes advantage of the multicast transmission to deliver the overlapped common content within users' viewports to reduce the bandwidth requirement. Our extensive experiments on a real testbed and with a trace-driven simulator show that M5 can effectively improve the frame rate by 44.1% and volumetric video quality by 62.3% compared to the state-of-the-art system.

Mobile cloud gaming enables high-end games on constrained devices by streaming the game content from powerful servers through mobile networks. Mobile networks suffer from highly variable bandwidth, latency, and losses that affect the gaming experience. This paper introduces , an end-to-end cloud gaming framework to minimize the impact of network conditions on the user experience. relies on an end-to-end distortion model adapting the video source rate and the amount of frame-level redundancy based on the measured network conditions. As a result, it minimizes the motion-to-photon (MTP) latency while protecting the frames from losses. We fully implement and evaluate its performance against the state-of-the-art techniques and latest research in real-time mobile cloud gaming transmission on a physical testbed over emulated and real wireless networks. consistently balances MTP latency (<140 ms) and visual quality (>31dB) even in highly variable environments. A user experiment confirms that maximizes the user experience with high perceived video quality, playability, and low user load.

In this paper, we present the design, implementation, and evaluation of SEAR, a collaborative framework for Scaling Experiences in multi-user Augmented Reality (AR). Most AR systems benefit from computer vision (CV) algorithms to detect, classify, or recognize physical objects for augmentation. A widely used acceleration method for mobile AR is to offload the compute-intensive tasks (e.g., CV algorithms) to the network edge. However, we show that the end-to-end latency, an important metric of mobile AR, may dramatically increase when offloading AR tasks from a large number of concurrent users to the edge. SEAR tackles this scalability issue through the innovation of a lightweight collaborative local caching scheme. Our key observation is that nearby AR users may share some common interests, and may even have overlapped views to augment (e.g., when playing a multi-user AR game). Thus, SEAR opportunistically exchanges the results of offloaded AR tasks among users when feasible and leverages compute resources on mobile devices to relieve, if necessary, the edge workload by intelligently reusing these results. We build a prototype of SEAR to demonstrate its efficacy in scaling AR experiences. We conduct extensive evaluations through both real-world experiments and trace-driven simulations. We observe that SEAR not only reduces the end-to-end latency, by up to 130×, compared to the state-of-the-art adaptive edge offloading scheme, but also achieves high object-recognition accuracy for mobile AR.

Although existing work has demonstrated the feasibility of streaming volumetric content to a single user, there exist many appealing applications (e.g., classroom education and collaborative design) that involve multiple users who watch the same volumetric content simultaneously. In this paper, we first perform a scaling experiment to demonstrate the challenges of streaming high-quality volumetric videos to multiple users and reveal the viewport-similarity opportunity that we can leverage to effectively optimize the network resource utilization using multicast over mmWave. We then develop a holistic research agenda for improving the performance and quality of experience for multi-user volumetric video streaming on commodity devices. Our proposed research includes joint viewport prediction and blockage mitigation for multiple users, multicast grouping based on viewport similarity, customized mmWave beam design for efficient multicast, and mmWave-aware multi-user video rate adaptation. Finally, we discuss the open challenges of building a practical system with the proposed research roadmap.

In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. Such an immense resolution makes streaming video-on-demand (VoD) content extremely resource-demanding. To tackle this challenge, introduces a novel framework that leverages an edge server to perform efficient, intelligent, and quality-guaranteed content transcoding, by extracting from panoramic frames the viewport stream that will be delivered to the client. To support real-time transcoding of 16K content, employs several key mechanisms such as dual-GPU acceleration, lossless viewport extraction, deep viewport prediction, and a two-layer streaming design. Our extensive evaluations using real users’ viewport movement data indicate that outperforms existing solutions, and can smoothly stream 16K panoramic videos to mobile devices over diverse wireless networks including WiFi, LTE, and mmWave 5G.

In the past few years, the computer vision community has developed numerous novel technologies of 3D vision (e.g., 3D object detection and classification and 3D scene segmentation). In this work, we explore the opportunities brought by these innovations for enabling real-time 3D vision on mobile devices. Mobile 3D vision finds various use cases for emerging applications such as autonomous driving, drone navigation, and augmented reality (AR). The key differences between 3D vision and 2D vision mainly stem from the input data format (i.e., point clouds or 3D meshes vs. 2D images). Hence, the key challenge of 3D vision is that it is could be more computation intensive and memory hungry than 2D vision, due to the additional dimension of input data. For example, our preliminary measurement study of several state-of-the-art machine learning models for 3D vision shows that none of them can execute faster than one frame per second on smartphones. Motivated by these challenges, we present in this position paper a research agenda on offering systems support for real-time mobile 3D vision, focusing on improving its computation efficiency and memory utilization.

Three-Dimensional (3D) semantic segmentation is an essential building block for interactive Augmented Reality (AR). However, existing Deep Neural Network (DNN) models for segmenting 3D objects are not only computation-intensive but also memory heavy, hindering their deployment on resource-constrained mobile devices. We present the design, implementation and evaluation of Slimmer, a generic and model-independent framework for accelerating 3D semantic segmentation and facilitating its real-time applications on mobile devices. In contrast to the current practice that directly feeds a point cloud to DNN models, Slimmer is motivated by our observation that these models remain high accuracy even if we remove a fraction of points from the input, which can significantly reduce the inference time and memory usage of these models. Our design of Slimmer faces two key challenges. First, the simplification method of point clouds should be lightweight. Otherwise, the reduced inference time may be canceled out by the incurred overhead of input-data simplification. Second, Slimmer still needs to accurately segment the removed points from the input to create a complete segmentation of the original input, again, using a lightweight method. Our extensive performance evaluation demonstrates that, by addressing these two challenges, Slimmer can dramatically reduce the resource utilization of a representative DNN model for 3D semantic segmentation. For example, if we can tolerate 1% accuracy loss, the reduction could be ~20% for inference time and ~9% for memory usage. The reduction increases to around ~27% for inference time and ~15% for memory usage when we can tolerate 2% accuracy loss.

In this paper, we perform a first comprehensive study of mobile volumetric video streaming. Volumetric videos are truly 3D, allowing six degrees of freedom (6DoF) movement for their viewers during playback. Such flexibility enables numerous applications in entertainment, healthcare, education, etc. However, volumetric video streaming is extremely bandwidth-intensive. We conduct a detailed investigation of each of the following aspects for point cloud streaming (a popular volumetric data format): encoding, decoding, segmentation, viewport movement patterns, and viewport prediction. Motivated by the observations from the above study, we propose ViVo, which is to the best of our knowledge the first practical mobile volumetric video streaming system with three visibility-aware optimizations. ViVo judiciously determines the video content to fetch based on how, what and where a viewer perceives for reducing bandwidth consumption of volumetric video streaming. Our evaluations over real wireless networks (including commercial 5G), mobile devices and users indicate that ViVo can save on average 40% of data usage (up to 80%) with virtually no drop in visual quality.

Volumetric videos allow viewers to exercise 6-DoF (degrees of freedom) movement when watching them. Due to their true 3D nature, streaming volumetric videos is highly bandwidth demanding. In this work, we present to our knowledge a first volumetric video streaming system that leverages deep super resolution (SR) to boost the video quality on commodity mobile devices. We propose a series of judicious optimizations to make SR efficient on mobile devices.

Traffic migration is a common procedure performed by operators during planned maintenance and unexpected incidents to prevent/reduce service disruptions. However, current practices of traffic migration often couple operators' intentions (e.g. device upgrades) with network setups (e.g. load-balancers), resulting in poor re-usability and substantial operational complexities. Our study of 205 Methods of Procedure (MOPs) from a major U.S. carrier suggests that generalizing traffic migration with a unified model is feasible. Such generalization along with SDN's automation capability is key to scalable and flexible management of traffic, especially for virtualized network functions with unprecedented scale, heterogeneity, and fast iteration. In this paper, we propose Egret, a generic traffic migration system that simplifies traffic management for physical and virtual network functions. Egret (1) hides intricate implementation details from operators with generic intention-based interfaces, and (2) modularizes common traffic migration procedures to enable plug-and-play by developers and vendors. Leveraging a novel mask-based abstraction of traffic migration jobs, Egret can further simplify reverse traffic migration and enable job interleaving.

In this paper, we study the problem of predicting a user's viewport movement in a networked VR system (i.e., predicting which direction the viewer will look at shortly). This critical knowledge will guide the VR system through making judicious content fetching decisions, leading to efficient network bandwidth utilization (e.g., up to 35% on LTE networks as demonstrated by our previous work) and improved Quality of Experience (QoE). For this study, we collect viewport trajectory traces from 275 users who have watched popular 360° panoramic videos for a total duration of 156 hours. Leveraging our unique datasets, we compare viewport movement patterns of different interaction modes: wearing a head-mounted device, tilting a smartphone, and dragging the mouse on a PC. We then apply diverse machine learning algorithms - from simple regression to sophisticated deep learning that leverages crowd-sourced data - to analyze the performance of viewport prediction. We find that the deep learning approach is robust for all interaction modes and yields supreme performance, especially when the viewport is more challenging to predict, e.g., for a longer prediction window, or with a more dynamic movement. Overall, our analysis provides key insights on how to intelligently perform viewport prediction in networked VR systems.

Personalized live video streaming is an increasingly popular technology that allows a broadcaster to share videos in real time with worldwide viewers. Compared to video-on-demand (VOD) streaming, experimenting with personalized live video streaming is harder due to its intrinsic live nature, the need for worldwide viewers, and a more complex data collection pipeline. In this paper, we make several contributions to both experimenting with and understanding today's commercial live video streaming services. First, we develop LIME (Live video MEasurement platform), a generic and holistic system allowing researchers to conduct crowd-sourced measurements on both commercial and experimental live streaming platforms. Second, we use LIME to perform, to the best of our knowledge, a first study of personalized 360° live video streaming on two commercial platforms, YouTube and Facebook. During a 7-day study, we have collected a dataset from 548 paid Amazon Mechanical Turk viewers from 35 countries who have watched more than 4,000 minutes of 360° live videos. Using this unique dataset, we characterize 360° live video streaming performance in the wild. Third, we conduct controlled experiments through LIME to shed light on how to make 360° live streaming (more) adaptive in the presence of challenging network conditions.

In this demo, we showcase how to stream 8K 360° video to commodity mobile devices without pre-processing or viewpoint prediction using our MEC-VR system. Users can freely select 360° video from YouTube and immediately become immersed in the video of 8K resolution using a Samsung GearVR compatible smartphone. Our system can support zoom in and zoom out while saving up to 80% bandwidth.

In this paper, we present the design, implementation and evaluation of Jaguar, a mobile Augmented Reality (AR) system that features accurate, low-latency, and large-scale object recognition and flexible, robust, and context-aware tracking. Jaguar pushes the limit of mobile AR's end-to-end latency by leveraging hardware acceleration with GPUs on edge cloud. Another distinctive aspect of Jaguar is that it seamlessly integrates marker-less object tracking offered by the recently released AR development tools (e.g., ARCore and ARKit) into its design. Indeed, some approaches used in Jaguar have been studied before in a standalone manner, e.g., it is known that cloud offloading can significantly decrease the computational latency of AR. However, the question of whether the combination of marker-less tracking, cloud offloading and GPU acceleration would satisfy the desired end-to-end latency of mobile AR (i.e., the interval of camera frames) has not been eloquently addressed yet. We demonstrate via a prototype implementation of our proposed holistic solution that Jaguar reduces the end-to-end latency to ~33 ms. It also achieves accurate six degrees of freedom tracking and 97% recognition accuracy for a dataset with 10,000 images.

Flare is a practical system for streaming 360-degree videos on commodity mobile devices. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. We conduct an IRB-approved user study where we collect head movement traces from 130 diverse users to gain insights on how to design the viewport prediction mechanism for Flare. We then develop novel online algorithms that determine which spatial portions to fetch and their corresponding qualities. We also innovate other components in the streaming pipeline such as decoding and server-side transmission. Through extensive evaluations (~400 hours' playback on WiFi and ~100 hours over LTE), we show that Flare significantly improves the QoE in real-world settings. Compared to non-viewport-adaptive approaches, Flare yields up to 18x quality level improvement on WiFi, and achieves high bandwidth reduction (up to 35%) and video quality enhancement (up to 4.9x) on LTE.

Flare is a practical system for streaming 360-degree videos on smartphones. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. Flare consists of a novel framework for the end-to-end streaming pipeline, introduces innovative streaming algorithms, and brings numerous system-level optimizations. In our demo, we will show that Flare substantially outperforms traditional viewport-agnostic streaming algorithms in terms of the video quality. We will also invite the audience to use Flare to watch attractive 360-degree videos.

Jaguar is a mobile Augmented Reality (AR) framework that leverages GPU acceleration on edge cloud to push the limit of end-to-end latency for AR systems and enable accurate and large-scale object recognition based on image retrieval. It integrates the emerging AR development tools (e.g., ARCore and ARKit) into its client design for achieving flexible, robust and context-aware object tracking. Our prototype implementation of Jaguar reduces the end-to-end AR latency to ~33 ms and achieves accurate six degrees of freedom (6DoF) tracking. In this demo, we will show that our Jaguar client recognizes movie posters within the camera view by offloading computation intensive tasks to edge cloud and augments these posters with their movie trailers in 3D upon receiving the recognition results.

360-degree videos are becoming increasingly popular on commercial platforms. In this position paper, we propose a holistic research agenda aiming at improving the performance, resource utilization efficiency, and users' quality of experience (QoE) for 360° video streaming on commodity mobile devices. Based on a Field-of-View (FoV) guided approach that fetches only portions of a scene that users will see, our proposed research includes the following: robust video rate adaptation with incremental chunk upgrading, big-data-assisted head movement prediction and rate adaptation, novel support for multipath streaming, and enhancements to live 360° video broadcast. We also show preliminary results demonstrating promising performance of our proof-of-concept 360° video streaming system on which our proposed research are being prototyped, integrated, and evaluated.

In HTTP/1.1, it is necessary for the client to request an object (e.g. an image in a page) in order for the server to send it, even if the server knows in advance what the client will need. Server Push is a feature introduced in HTTP/2 that promises to improve page load times (PLT) by having the server push content to the browser in advance. In this paper, we investigate the benefits and challenges of using Server Push on mobile devices. We first examine whether pushing all content or just the CSS and Javascript files performs better, and find the former leads to much better web performance. Also, we find that sites making use of domain sharding or which otherwise have content divided across many servers do not benefit much from Server Push, a major challenge for Server Push going forward. Network performance characteristics also play a major role. Server Push is especially effective at improving performance at high loss rates (16% median PLT reduction with a 2% loss rate) and high latencies (14% PLT reduction with 100 ms latency), and has little benefit for high-speed Ethernet connections. This motivates its use on mobile devices, although we also find the limited processing power of these devices limits the benefits of Server Push. Server Push also offers modest energy benefits, with energy savings of 9% on LTE for one device. Overall, Server Push is a promising approach for improving web performance in mobile networks, but there are a number of challenges in achieving the full benefits of Server Push.

Compared with using only a single wireless path such as WiFi, leveraging multipath (e.g., WiFi and cellular) can dramatically improve users' quality of experience (QoE) for mobile video streaming. However, Multipath TCP (MPTCP), the de-facto multipath solution, lacks the support to prioritize one path over another. When applied to video streaming, it may cause undesired network usage such as substantial over-utilization of the metered cellular link. In this paper, we propose MP-DASH, a multipath framework for video streaming with the awareness of network interface preferences from users. The basic idea behind MP-DASH is to strategically schedule video chunks' delivery and thus satisfy user preferences. MP-DASH can work with a wide range of off-the-shelf video rate adaptation algorithms with very small changes. Our extensive field studies at 33 locations in three U.S. states suggest that MP-DASH is very effective: it can reduce cellular usage by up to 99% and radio energy consumption by up to 85% with negligible degradation of QoE, compared with off-the-shelf MPTCP.

Leveraging multiple interfaces (e.g., WiFi and cellular) on mobile devices is known to provide performance gains for applications such as bulk data transfer. In this paper, we complement prior studies by conducting, to the best of our knowledge, the first measurement study of mobile web performance over multipath TCP (MPTCP). Based on experiments using real web pages under diverse settings, we found the upper-layer web protocols can incur complex and sometimes unexpected interactions with the multipath-enabled transport layer. MPTCP always boosts SPDY while may hurt HTTP. MPTCP also helps mitigate SPDY's key limitations of being vulnerable to losses and inefficient bandwidth utilization. We provide in-depth explanations of the root causes, as well as recommendations for improving mobile web performance over MPTCP.

In this paper, we propose an energy efficient device discovery protocol, eDiscovery, as the first step to bootstrapping opportunistic communications for smartphones, the most popular mobile devices. We chose Bluetooth over WiFi as the underlying wireless technology of device discovery, based on our measurement study of their operational power at different states on smartphones. eDiscovery adaptively changes the duration and interval of Bluetooth inquiry in dynamic environments, by leveraging history information of discovered peers. We implement a prototype of eDiscovery on Nokia N900 smartphones and evaluate its performance in three different environments. To the best of our knowledge, we are the first to conduct extensive performance evaluation of Bluetooth device discovery in the wild. Our experimental results demonstrate that compared with a scheme with constant inquiry duration and interval, eDiscovery can save around 44 percent energy at the expense of discovering only about 21 percent less peers. The results also show that eDiscovery performs better than other existing schemes, by discovering more peers and consuming less energy. We also verify the experimental results through extensive simulation studies in the ns-2 simulator.

Cellular providers are rapidly deploying multiple technologies like cell biasing, carrier aggregation, co-ordinated interference control/scheduling to improve capacity and coverage. In this paper, we explore a complementary transport layer approach based on multipath TCP that can concurrently use multiple interfaces to boost throughput of users with poor coverage and improve fairness. Multipath TCP has been recently standardized by IETF and requires no modifications to applications. It has been shown to improve fairness and throughput in wire line environments and individual user throughputs in wireless networks. However, in a wireless multi-user environment, it is not clear that it is always beneficial, as we show in this paper. Therefore, we examine if it is indeed beneficial for a service provider to judiciously decide whether to enable multiple cellular interfaces on a smart phone based on a global centralized view of its network. Alternatively, should a device decide independently based only on a local view? To quantify the network wide impact in a system where users have multiple cellular interfaces, we have developed centralized and distributed heuristic algorithms to evaluate this, particularly in the context of fairness across all the users. Our simulations and numerical models show that there are potential gains in fairness (15-30%) to be realized by judiciously enabling multipath connections at the cell edge. These gains diminish as the number of users in a cell increases or users behave greedily. We also quantify the delicate balance between throughput and fairness. Our analysis provides an intuition on which user(s) in a cellular network stand to benefit the most by enabling multiple interfaces. We also discuss LTE protocol mechanisms to enforce associations of specific interfaces to specific cells.

In this paper, we investigate the problem of identifying influential users in mobile social networks. Influential users are individuals with high centrality in their social-contact graphs. Traditional approaches find these users through centralized algorithms. However, the computational complexity of these algorithms is known to be very high, making them unsuitable for large-scale networks. We propose a lightweight and distributed protocol, iWander, to identify influential users through fixed-length random-walk sampling. We prove that random-walk sampling with O(logn) steps, where n is the number of nodes in a graph, comes quite close to sampling vertices approximately according to their degrees. To the best of our knowledge, we are the first to design a distributed protocol on mobile devices that leverages random walks for identifying influential users, although this technique has been used in other areas. The most attractive feature of iWander is its extremely low control-message overhead, which lends itself well to mobile applications. We evaluate the performance of iWander for two applications, targeted immunization of infectious diseases and target-set selection for information dissemination. Through extensive simulation studies using a real-world mobility trace, we demonstrate that targeted immunization using iWander achieves a comparable performance with a degree-based immunization policy that vaccinates users with a large number of contacts first, while generating only less than 1% of this policy's control messages. We also show that target-set selection based on iWander outperforms the random and degree-based selections for information dissemination in several scenarios.

Searching for mobile data offloading solutions has been topical in recent years. In this paper, we present a collaborative WiFi-based mobile data offloading architecture - Metropolitan Advanced Delivery Network (MADNet), targeting at improving the energy efficiency for smartphones. According to our measurements,WiFi-based mobile data offloading for moving smartphones is challenging due to the limitation ofWiFi antennas deployed on existing smartphones and the short contact duration with WiFi APs. Moreover, our study shows that the number of open-accessible WiFi APs is very limited for smartphones in metropolitan areas, which significantly affects the offloading opportunities for previous schemes that use only open APs. To address these problems, MADNet intelligently aggregates the collaborative power of cellular operators, WiFi service providers and end-users. We design an energy-aware algorithm for energy-constrained devices to assist the offloading decision. Our design enables smartphones to select the most energy efficient WiFi AP for offloading. The experimental evaluation of our prototype on smartphone (Nokia N900) demonstrates that we are able to achieve more than 80% energy saving. Our measurement results also show that MADNet can tolerate minor errors in localization, mobility prediction, and offloading capacity estimation.

In this paper, we propose an energy efficient device discovery protocol, eDiscovery, as the first step to bootstrapping opportunistic communications for smartphones, the most popular mobile devices. We chose Bluetooth over WiFi as the underlying wireless technology of device discovery, based on our measurement study of their energy consumption on smartphones. eDiscovery adaptively changes the duration and interval of Bluetooth inquiry in dynamic environments, by leveraging history information of discovered peers. We implement a prototype of eDiscovery on Nokia N900 smartphones and evaluate its performance in three different environments. To the best of our knowledge, we are the first to conduct extensive performance evaluation of Bluetooth device discovery in the wild. Our experimental results demonstrate that compared with a scheme with constant inquiry duration and interval, eDiscovery can save around 44% energy at the expense of discovering only about 21% less peers. The results also show that eDiscovery performs better than other existing schemes, by discovering more peers and consuming less energy.

In this paper, we study the problem of identifying influential users in mobile social networks. Traditional approaches find these users through centralized algorithms on either friendship or social-contact graphs of all users. However, the computational complexity of these algorithms is known to be very high, making them unsuitable for large-scale networks. We propose a lightweight and distributed protocol, iWander, to identify influential users through fixed-length random walks. To the best of our knowledge, we are the first to design a distributed protocol on smartphones that leverages random walks for identifying influential mobile users, although this technique has been used in other areas.

3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution to partially solve this problem, because there is almost no monetary cost for it. We propose to exploit opportunistic communications to facilitate information dissemination in the emerging Mobile Social Networks (MoSoNets) and thus reduce the amount of mobile data traffic. As a case study, we investigate the target-set selection problem for information delivery. In particular, we study how to select the target set with only k users, such that we can minimize the mobile data traffic over cellular networks. We propose three algorithms, called Greedy, Heuristic, and Random, for this problem and evaluate their performance through an extensive trace-driven simulation study. Our simulation results verify the efficiency of these algorithms for both synthetic and real-world mobility traces. For example, the Heuristic algorithm can offload mobile data traffic by up to 73.66 percent for a real-world mobility trace. Moreover, to investigate the feasibility of opportunistic communications for mobile phones, we implement a proof-of-concept prototype, called Opp-off, on Nokia N900 smartphones, which utilizes their Bluetooth interface for device/service discovery and content transfer.

Recently, practical subframe-level schemes, such as frame combining and partial packet recovery, have been proposed for combating wireless transmission errors. These approaches depend heavily on the bit error behavior of wireless data transmissions, which is overlooked in the literature. We study the characteristics of subframe bit errors and their location distribution by conducting extensive experiments on several IEEE 802.11 WLAN testbeds. Our measurement results identify three bit error patterns: slope-line, saw-line, and finger. Among these three patterns, we have verified that the slope-line and saw-line are present in different physical environments and across various hardware platforms. However, the finger pattern does not appear on some platforms. We discuss our current hypotheses for the reasons behind these bit error patterns and how identifying these patterns may help improve the robustness of WLAN transmissions. We believe that identifiable bit error patterns can potentially introduce new opportunities in channel coding, network coding, forward error correction (FEC), and frame combining.

Cellular networks are currently facing the challenges of mobile data explosion. High-end mobile phones and laptops double their mobile data traffic every year and this trend is expected to continue given the rapid development of mobile social applications. It is imperative that novel architectures be developed to handle such voluminous mobile data. In this paper, we propose and evaluate an integrated architecture exploiting the opportunistic networking paradigm to migrate data traffic from cellular networks to metropolitan WiFi access points (APs). To quantify the benefits of deploying such an architecture, we consider the case of bulk file transfer and video streaming over 3G networks and simulate data delivery using real mobility data set of 500 taxis in an urban area. We are the first to quantitatively evaluate the gains of city-wide WiFi offloading using large scale real traces. Our results give the numbers of APs needed for different requirements of quality of service for data delivery in large metropolitan area. We show that even with a sparse WiFi network the delivery performance can be significantly improved. This effort serves as an important feasibility study and provides guidelines for operators to evaluate the possibility and cost of this solution.

Multiple co-channel WLAN BSSes (i.e., WLAN cells) overlapping in coverage are generally considered undesirable because members of the OBSSes compete for channel access, which typically increases the contention level of wireless medium access and reduces overall system performance. In this paper, we propose to use channel access throttling (CAT) for managing Wireless LAN radio resources for overlapping BSSes (OBSSes). CAT provides an access point (AP) of each BSS with a mechanism to control channel access parameters of its member stations on the fly. By coordinating the CAT operations of the OBSS APs, we can enable privileged channel access to an individual BSS at a particular time, for example, by assigning high priority access parameters to member stations associated with the BSS. By controlling how much each BSS may be given the privileged channel access, we can also achieve a proportional partitioning of channel capacity among OBSSes. We present evaluation results obtained from both simulations and experiments using testbed built with commercial off-the-shelf (COTS) WLAN hardware and open-source device driver. Our results show that with CAT, not only can we proportionally partition channel capacity among the OBSSes, but also improve channel utilization efficiency and increase overall capacity.

In IEEE 802.11 Wireless LAN (WLAN) systems, techniques such as acknowledgement, retransmission, and transmission rate adaptation, are frame-level mechanisms designed for combating transmission errors. Recently sub-frame level mechanisms such as frame combining have been proposed by the research community. In this paper, we present results obtained from our bit error study for identifying sub-frame error patterns because we believe that identifiable bit error patterns can potentially introduce new opportunities in channel coding, network coding, forward error correction (FEC), and frame combining mechanisms. We have constructed a number of IEEE 802.11 wireless LAN testbeds and conducted extensive experiments to study the characteristics of bit errors and their location distribution. Conventional wisdom dictates that bit error probability is the result of channel condition and ought to follow corresponding distribution. However our measurement results identify three repeatable bit error patterns that are not induced by channel conditions. We have verified that such error patterns are present in WLAN transmissions in different physical environments and across different wireless LAN hardware platforms. We also discuss our current hypotheses for the reasons behind these bit error probability patterns and how identifying these patterns may help improving WLAN transmission robustness.

Equipping wireless nodes with multiple radios can significantly increase the capacity of wireless networks, by making these radios simultaneously transmit over multiple non-overlapping channels. However, due to the limited number of radios and available orthogonal channels, designing efficient channel assignment and scheduling algorithms in such networks is a major challenge. In this paper, we present provably-good distributed algorithms for simultaneous channel allocation of individual links and packet-scheduling, in software-defined radio (SDR) wireless networks. Our distributed algorithms are very simple to implement, and do not require any coordination even among neighboring nodes. A novel access hash function or random oracle methodology is one of the key drivers of our results. With this access hash function, each radio can know the transmitters' decisions for links in its interference set for each time slot without introducing any extra communication overhead between them. Further, by utilizing the inductive-scheduling technique, each radio can also backoff appropriately to avoid collisions. Extensive simulations demonstrate that our bounds are valid in practice.

The de facto QoS channel access method for the IEEE 802.11 Wireless LANs is the Enhanced Distributed Channel Access (EDCA) mechanism, which differentiates transmission treatments for data frames belonging to different traffic categories with four different levels of channel access priority. In this paper, we propose extending EDCA with Channel Access Throttling (CAT) for more flexible and efficient QoS support. By assigning different member stations different channel access parameters, CAT differentiates channel access priorities not between traffic categories but between member stations. Then by dynamically changing the channel access parameters of each member station based on a pre-computed schedule, CAT enables EDCA WLANs the benefits of scheduled access QoS. We also present evaluation results of CAT obtained from both simulations and experiments conducted using off-the-shelf WLAN hardware and open-source device driver. Our results show that CAT can proportionally partition channel capacity, significantly improve performance of multimedia applications, effectively achieve performance protection for admitted flows, and increase per cell VoIP call capacity by up to 41%.

In IEEE 802.16 networks, initial ranging is a primary and important procedure of connection setup between subscriber stations and base station. The mandatory method defined in the standard of contention resolution is based on a truncated binary exponential backoff, with a fixed initial contention window size. However, the original algorithm neglects the possibility that the number of actively contending stations may change dynamically over time, leading to dynamically changing contention intensity. The major contribution of this paper is twofold: 1) we propose an accurate analytical model to analyze the performance of initial ranging requests in IEEE 802.16 networks. Two metrics, connection probability and average connection delay, are investigated to evaluate the network performance; 2) based on the above analysis, we propose an efficient performance improvement method by using dynamic window adjustment for initial ranging. Unlike the standard algorithm, this algorithm automatically adjusts the initial contention window to an optimal trade-off point between connection probability and connection delay. The performance revels that improving the service capacity and buffer size of base station can optimize the connection probability and the average connection delay. The numerical results also show that the optimal contention window adjustment outperforms the algorithm in the standard.

In most of the proposed clustering algorithms for wireless ad hoc networks, the cluster-heads form a dominating set in the network topology. A variant ofdominatingsetwhich is more suitable for cluster formation is theweakly-connecteddominatingset(WCDS). We propose an area based distributed algorithm for WCDS formation with time and message complexityO(n). In thisAreaalgorithm, we partition the wireless nodes into different areas, use some deterministic criteria to select the nodes for the WCDS in each area and adjust the area borders by adding additional nodes to the final WCDS. The effectiveness of our algorithm is confirmed through analysis and comprehensive simulation study.

In this paper, we propose an accurate analytical model to analyze the performance of initial ranging requests in IEEE 802.16 networks. Two metrics, connection probability and average connection delay, are investigated to evaluate the network performance. Performance observation demonstrate that the connection probability is not heavily influenced by the contention window size and reconnection retry limitation but the average connection delay is sensitive to the above two parameters. Moreover, we find that improving the service capacity and buffer size of base station can optimize the connection probability and average connection delay

We propose an efficient centralized scheduling algorithm in IEEE 802.16 based wireless mesh networks (WMN) to provide high qualified wireless multimedia services. Our algorithm takes special attention on the relay function of the mesh nodes in a transmission tree which is seldom studied in previous research. Some important design metrics, such as fairness, channel utilization and transmission delay are considered in this scheduling algorithm. IEEE 802.16 employs TDMA and the selection policy for scheduled links in a time slot will definitely impact the system performance. We evaluated the proposed algorithm with four selection criteria through extensive simulations and the results are instrumental for improving the performance of IEEE 802.16 based WMNs in terms of link scheduling

To efficiently manage ad hoc networks, this paper proposes a novel distributed algorithm for connected dominating set (CDS) formation in wireless ad hoc networks with time and message complexity O(n). This Area algorithm partitions the nodes into different areas and selectively connects two dominators that are two or three hops away. Compared with previous well-known algorithms, we confirm the effectiveness of this algorithm through analysis and comprehensive simulation study. The number of nodes in the CDS formed by this Area algorithm is up to around 55% less than that constructed by others.

Connected dominating set based routing is a promising approach for enhancing the routing efficiency in wireless ad hoc networks. However, finding the minimum dominating set in an arbitrary graph is a NP-hard problem. We propose a simple and efficient distributed algorithm for constructing a connected dominating set in wireless ad hoc networks with time complexity O(n) and message complexity O(nlog n). The dominating set generated with our algorithm can be more reliable and load balanced for routing as compared with some well-known algorithms. The simulation results demonstrate that our algorithm outperforms previous work in terms of the size of the resultant connected dominating set.