Volumetric video promises rich 6DoF experiences, but current 3D Gaussian Splatting (3DGS) pipelines are inefficient for dynamic content: each frame is trained independently, ignoring temporal redundancy, which leads to high storage and bandwidth requirements. In this paper, we propose CubeGS, a lightweight, streaming-oriented compression mechanism for 3DGS video that preserves visual fidelity and rendering efficiency of 3DGS while substantially reducing the data footprint. CubeGS organizes frames using a Group-of-Pictures (GoP) structure, introducing reference frames and predictive frames within dynamic 3DGS sequences. Central to our design is cube-wise motion residual capturing, which reuses Gaussian parameters across time and encodes only localized changes in appearance and geometry. To further reduce storage and transmission cost, we integrate a modified Draco geometry encoder tailored to Gaussian parameterization. Together, these components make on-demand Gaussian Splatting streaming feasible over commercial networks with CubeGS averaging 193 Mbps for streaming a 24 fps video. CubeGS achieves approximately 7.7× data reduction and up to 16.9% higher video quality compared to 3DGStream, the state-of-the-art dynamic 3DGS method. Importantly, CubeGS retains the rendering speed of 3DGS while scaling efficiently to complex dynamic scenes with much lower storage and bandwidth overhead than existing approaches.

Swiping the screen to switch videos is a unique browsing behavior for short videos, intended to facilitate viewers in quickly searching for content of interest. However, frequent video switching can result in nearly half of the data being used to transmit never-watched video data, leading to unnecessary network load via resource wastage. To tackle this problem, recent studies have utilized historical viewing data to predict the necessary length of videos to download based on viewing probability. Critically, the precision of the predictions plays a pivotal role in shaping both data consumption and user experience. This paper addresses issues that emerge with inaccurate predictions by proposing KBL (Kettle-style Buffer Loading), a novel algorithm to balance waste with a high-quality video experience, without requiring extensive training or prior knowledge. Inspired by tea kettle service, KBL reduces waste by setting the boundary of the respective video buffers, current and pre-loaded videos, based on an evaluation of the network conditions. Through extensive evaluation, KBL is demonstrated to reduce waste by up to 58% of data usage compared to state of the art short video strategies without incurring significant QoE degradation, even in the face of shifting user behavior.

Recent improvements in adaptive video streaming have been significant in the areas of adaptation algorithms and encoding (e.g., emergence of AV1 codec). A promising but yet unexplored feature of AV1 is the switching frames (S-frames). In this paper, we explore the pros and cons of S-frames in DASH streaming and demonstrate that S-frames can improve the compression and quality switching capabilities of AV1. We conduct a measurement study to understand the extent of benefits of S-frames and find that 10% – 50% data savings are achievable for many encoding configurations where S-frame use is maximized. We further propose a stream structure where the number of S-frames used balances QoE improvement with the ability to play or seek randomly within the video in challenging network conditions. We evaluate the streams using S-frames in a DASH player under a variety of conditions. We find that when S-frame use and switching opportunities are both maximized, they significantly improve stall performance, the critical aspect of the Quality of Experience (QoE), by reducing stall time by at least 88 % in realistic bandwidth conditions.

Video conferencing applications (VCAs) are a vital tool for business, education, and other important purposes. However, VCAs are vulnerable to network latency, which can cause issues in client interactivity, such as increased overlaps, longer silence durations, and a degradation of the turn-taking structure. In this paper, we propose two systems for improving VCA interactivity in the presence of high network latency: one based on adjustment of the latency between pairs of clients, and the other based on notifying clients of their high latency. Both systems are suitable for deployment within the VCA selective forwarding unit, the central server for the conference. We evaluate the systems using a client behavioral model with accompanying interactivity metrics, and show, among other results, improvements of up to $50 \%$ in the overlap rate and $40 \%$ in useful conversation time, as well as restoration of the turn taking structure to the state with no network latency.

Delivering popular short video streaming services like TikTok, Instagram Reels, or YouTube Shorts, poses substantial challenges for service providers and network operators. This is not only due to high download volumes but also due to high-volume pre-loading strategies that cause high bandwidth demand variations. These strategies, designed to reduce initial delays, can additionally lead to bandwidth excess when users swipe quickly through videos and consume only a fraction of downloaded content. This creates inefficiencies and unbalanced network resource utilization, particularly in competitive bandwidth environments. To address these challenges, we investigate the effectiveness of bandwidth capping in this paper, i.e., limiting the throughput of video flows in the network. We conduct measurement studies to analyze the impact of capping on network interactions and Quality of Experience (QoE) of three popular short video services in different scenarios. We find that capping substantially reduces download volume (15% -- 45% median reduction) and bandwidth excess (18% -- 52% mean reduction), while bandwidth utilization fairness improves. Meanwhile, QoE surprisingly remains nearly unaffected in most cases, with only minor statistical differences in a few scenarios.

Video conferencing applications (VCAs) have become an indispensable tool for business, educational, and personal communications. There is, therefore, considerable interest in understanding and measuring the Quality of Experience (QoE) delivered by VCAs to their users. Video quality, one QoE measure, has received considerable attention in the literature. In this paper, we are concerned with another important aspect of VCA QoE, namely interactivity. We define this informally as the ability of a VCA to facilitate satisfying interaction among its users. Interactivity is primarily impacted by the media transmission latency among users which is, in turn, a function of network and application processing delays. Our goal in this work is to address two challenges in investigating interactivity-related QoE in VCAs. First, we propose a suite of meaningful quantifiable interactivity metrics, such as the proportion of silence time and rate of overlapping speech, that correlate well with conversational impairments and, hence, QoE perceptions. Second, we investigate scalable approaches for measuring these metrics. We develop a validated model for user behavior that enables realistic simulation of interactivity in VCA sessions. We also briefly consider an approach to measure interactivity metrics from packet traces. Through a set of experimental results, we demonstrate how our evaluation methodology provides a way for researchers, VCA service providers and network operators to perform large-scale investigations of how latency can interfere with user interactivity and impact VCA QoE.

Network throughput tests, commonly known as “speed tests” are widely used by consumers, regulators, and ISPs to measure and diagnose network performance. However, the tools used to conduct these tests are often costly in terms of data consumption. Moreover, the speed tests rely on data that is transferred to clients for the sole purpose of measuring throughput with the data being discarded and serving no other purpose. In this paper, we present rePurpose, a system that moves useful content (ads) to enable periodic speed tests by significantly offsetting the cost of network measurement, thereby avoiding harming user QoE. rePurpose can work within the existing ad ecosystem to pre-stage ads needed by users ahead of time. We evaluate the efficacy of rePurpose by emulating a common scenario where users watch videos and ads. Our evaluation shows that rePurpose can reduce the data cost of periodic speed tests by up to 90%. Moreover, by virtue of the time-shifted delivery courtesy of the periodic speed tests moving useful data, rePurpose improves video and ad QoE by reducing or eliminating start-up delay by up to five seconds.

Active network probing, commonly known as a speed test, is the prevalent network speed measurement and diagnostic method. Speed tests primarily measure achievable throughput by conducting bulk downloads that saturate the bottleneck link. However, the impact of speed tests on user Quality of Experience (QoE) has not been thoroughly explored. In this paper, we investigate the effects of active network probing on user QoE during two common activities: file downloading and video streaming, focusing on key QoE metrics such as download time, video bitrate, and buffering. Our analysis reveals that the standard speed test significantly extends download times (by up to 88% in WiFi and 46% in cellular networks) and adversely affects various video QoE metrics, particularly bitrate, resulting in an average bitrate reduction ranging from 46% to 60%. Moreover, we assess the outcomes of typical speed test scenarios, such as single and double tests, and establish that both variants impair QoE, with double tests causing greater disruptions. Our findings offer a comprehensive insight into the ramifications of active network probing on user applications and emphasize the necessity for approaches to alleviate its detrimental effects on QoE.

Traditional Adaptive BitRate (ABR) streaming faces a challenge of providing smooth experience under highly variable network conditions, especially when low latency is required. Effective adaptation techniques exist for deep-buffer scenarios, such as streaming long-form Video-on-Demand content, but remain elusive for short-form or low-latency cases, when even a short segment may be delivered too late and cause a stall. Recently proposed temporal adaptation aims to mitigate this problem by being robust to losing a part of the video segment, essentially dropping the tail of the segment intentionally to avoid the stall. In this paper, we analyze this approach in the context of a recently adopted codec AV1 and find that it does not always provide the promised benefits. We investigate the root causes and find that a combination of codec efficiency and TCP behavior can defeat the benefits of temporal adaptation. We develop a solution based on QUIC, and present the results showing that the benefits of temporal adaptation that still apply to AV1, including reduced stall time up to 65% compared to the original TCP-based approach. In addition, we present a novel way to use the stream management features of QUIC to benefit Quality-of-Experience (QoE) and reduce wasted data in video streaming.

Short videos have recently emerged as a popular form of short-duration User Generated Content (UGC) within modern social media. Short video content is generally less than a minute long and predominantly produced in vertical orientation on smartphones. While still fundamentally being streaming, short video delivery is distinctly characterized by the deployment of a mechanism that pre-loads ahead of user request. Background pre-loading aims to eliminate start-up time, which is now prioritized higher in Quality of Experience (QoE) objectives, given that the application design facilitates instant 'swiping' to the next video in a recommended sequence. In this work, we provide a comprehensive comparison of four popular short video services. In particular, we explore content characteristics and evaluate the video quality across resolutions for each service. We next characterize the pre-loading policy adopted by each service. Last, we conduct an experimental study to investigate data consumption and evaluate achieved QoE under different network scenarios and application configurations.

Cellular networks provide an essential connectivity foundation for a sizable number of mobile devices and applications, making it compelling to measure their performance in regard to user experience. Although cellular infrastructure provides low-level mechanisms for network-specific performance measurements, there is still a distinct gap in discerning the actual application-level or user-perceivable performance from such methods. Put simply, there is little substitute for direct sampling and testing to measure end-to-end performance. Unfortunately, most existing technologies often fall quite short. Achievable Throughput tests use bulk TCP downloads to provide an accurate but costly (time, bandwidth, energy) view of network performance. Conversely, Available Bandwidth techniques offer improved speed and low cost but are woefully inaccurate when faced with the typical dynamics of cellular networks. In this paper, we propose CUP, a novel approach for Cellular Ultra-light Probe-based available bandwidth estimation that seeks to operate at the cost point of Available Bandwidth techniques while correcting accuracy issues by leveraging the intrinsic aggregation properties of cellular scheduling, coupled with intelligent packet timing trains and the application of Bayesian probabilistic analysis. By keeping the costs low with reasonable accuracy, our approach enables scaling both with respect to time (longitude) and space (user device density). We construct a CUP prototype to evaluate our approach under various demanding real-world cellular environments (longitudinal, driving, multiple vendors) to demonstrate the efficacy of our approach.

Understanding end-user video Quality of Experience (QoE) is important for Internet Service Providers (ISPs). Existing work presents mechanisms that use network measurement data to estimate video QoE. Most of these mechanisms assume access to packet-level traces, the most-detailed data available from the network. However, collecting packet-level traces can be challenging at a network-wide scale. Therefore, we ask:"Is it feasible to estimate video QoE with lightweight, readily-available, but coarse-grained network data?" We specifically consider data in the form of Transport Layer Security (TLS) transactions that can be collected using a standard proxy and present a machine learning-based methodology to estimate QoE. Our evaluation with three popular streaming services shows that the estimation accuracy using TLS transactions is high (up to 72%) with up to 85% recall in detecting low QoE (low video quality or high re-buffering) instances. Compared to packet traces, the estimation accuracy (recall) is 7% (9%) lower but has up to 60 times lower computation overhead.

Large data transfers can result in significant congestion and performance degradation for interactive end-user applications such as web browsing and streaming. While there are existing TCP congestion control algorithms for delivery of large volume data (e.g., LEDBAT, TCP-LP), our results show that these protocols are not effective in cellular networks due to variability in radio channel conditions and the use of cellular schedulers in base stations. We propose Legilimens, an agile TCP variant for cellular downlink transfers, which not only retains desirable properties of existing approaches, but also exploits the properties of the cellular schedulers to estimate load and capacity and addresses the challenges in cellular networks. As a result, Legilimens is able to deliver traffic using only the spare capacity on the downlink. We conduct extensive evaluations of Legilimens in multiple settings-in a large cellular network for real-world performance, on the PhantomNet emulator for controlled experiments, and ns-3 simulator for scaled experiments-all of which demonstrate that Legilimens is superior to existing protocols in transferring large volumes of data without interfering with regular user traffic. Compared to existing low-priority protocols, Legilimens improves the throughput of background flows by 2x on average (up to 5x) without degrading the performance of foreground flows across all the three testbeds.

Video dominates Internet traffic today. Users retrieve on-demand video from Content Delivery Networks (CDNs) which cache video chunks at front-ends. In this paper, we describe AViC, a caching algorithm that leverages properties of video delivery, such as request predictability and the presence of highly unpopular chunks. AViC's eviction policy exploits request predictability to estimate a chunk's future request time and evict the chunk with the furthest future request time. Its admission control policy uses a classifier to predict singletons --- chunks evicted before a second reference. Using real world CDN traces from a commercial video service, we show that AViC outperforms a range of algorithm including LRU, GDSF, AdaptSize and LHD. In particular LRU requires up to 3.5× the cache size to match AViC's performance. Further, AViC has low time complexity and has memory complexity comparable to GDSF.

A large variety of traffic - time-sensitive “foreground” traffic (e.g., web browsing) and time-insensitive “background” traffic (e.g., software updates) - compete for the scarce cellular bandwidth, especially on the downlink. While there is limited in-network support for traffic prioritization, existing endto-end, “low priority transport protocols” exhibit sub-optimal performance in cellular networks. We propose Sneaker, which yields to time-sensitive foreground traffic during periods of congestion and enables time-insensitive background traffic to efficiently utilize any spare capacity. Sneaker achieves the desired goal by randomly dropping packets coming into the base station, based on traffic type and network conditions. Our key contribution is the derivation of the optimal dropping rate and a practical dropping rate, which performs close to optimal. Further, Sneaker co-exists and performs well with existing cellular schedulers and transport protocols.

To cope with diverse network conditions, HTTP Adaptive Streaming (HAS) enables video players to dynamically change the video quality throughout the video stream. However, effective adaptation that minimizes stalls and start-up time while maximizing quality and stability remains elusive, especially when available bandwidth is variable or multiple players compete for the bottleneck capacity. Conventional approach to adaptation is to make a decision on the next video segment quality based on hysteresis of prior throughput measurements. This approach is not robust to bandwidth fluctuation at small time scales, which can consequently lead to stalls, bandwidth waste, and unstable quality, mainly due to the inability to mitigate significant bandwidth reduction during the segment download. We propose BETA- Bandwidth-Efficient Temporal Adaptation, an agile approach that allows HAS players to refine the quality level within video segments on the fly, according to the actual bandwidth conditions experienced while downloading each segment. We define a new HAS-oriented transmission order of video frames within segments that facilitates decodability of partial frames and paves the way for changing the paradigm from discrete to continuous bitrate ladders for HAS. BETA can work with any adaptation algorithm or HAS player to significantly improve robustness and efficiency in dynamic network environments and for low-latency streams, as well as to dramatically reduce content storage and encoding infrastructure requirements. Our evaluation using the real player implementation shows that BETA improves video quality by up to 20%, reduces number of stalls by 20%-100% in nearly 80% of cases, and cuts down wasted bandwidth by 22%-100%.

Today's HTTP adaptive streaming applications are designed to provide high levels of Quality of Experience (QoE) across a wide range of network conditions. The adaptation logic in these applications typically needs an estimate of the future network bandwidth for quality decisions. This estimation, however, is challenging in cellular networks because of the inherent variability of bandwidth and latency due to factors like signal fading, variable load, and user mobility. In this paper, we exploit machine learning (ML) techniques on a range of radio channel metrics and throughput measurements from a commercial cellular network to improve the estimation accuracy and hence, streaming quality. We propose a novel summarization approach for input raw data samples. This approach reduces the 90th percentile of absolute prediction error from 54% to 13%. We evaluate our prediction engine in a trace-driven controlled lab environment using a popular Android video player (ExoPlayer) running on a stock mobile device and also validate it in the commercial cellular network. Our results show that the three tested adaptation algorithms register improvement across all QoE metrics when using prediction, with stall reduction up to 85% and bitrate switching reduction up to 40%, while maintaining or improving video quality. Finally, prediction improves the video QoE score by up to 33%.

Understanding the user-perceived quality of experience (QoE) of HTTP-based video has become critical for content providers, distributors, and network operators. For network operators, monitoring QoE is challenging due to lack of access to video streaming applications, user devices, or servers. Thus, network operators need to rely on the network traffic to infer key metrics that influence video QoE. Furthermore, with content providers increasingly encrypting the network traffic, the task of QoE inference from passive measurements has become even more challenging. In this paper, we present a methodology called eMIMIC that uses passive network measurements to estimate key video QoE metrics for encrypted HTTP-based adaptive streaming (HAS) sessions. eMIMIC uses packet headers from network traffic to model an HAS session and estimate video QoE metrics, such as average bitrate and re-buffering ratio. We evaluate our methodology using network traces from a variety of realistic conditions and ground truth collected using a lab testbed for video sessions from three popular services, two video on demand (VoD) and one Live. eMIMIC estimates re-buffering ratio within 1% point of ground truth for up to 75% sessions in VoD (80% in Live) and average bitrate with error under 100 Kb/s for up to 80% sessions in VoD (70% in Live). We also compare eMIMIC with recently proposed machine learning-based QoE estimation methodology. We show that eMIMIC can predict average bitrate with 2.8%-3.2% higher accuracy and re-buffering ratio with 9.8%-24.8% higher accuracy without requiring any training on ground truth QoE metrics. Finally, we show that eMIMIC can estimate real-time QoE metrics with at least 89.6% accuracy in identifying buffer occupancy state and at least 85.7% accuracy in identifying average bitrate class of recently downloaded chunks.

Due to widespread popularity of streaming services, many streaming clients typically compete over bottleneck links for their own bandwidth share. However, in such environments, the rate adaptation algorithms used by modern streaming clients often result in instability and unfairness, which negatively affects the playback experience. In addition, mobile clients often waste bandwidth by trying to stream excessively high video bitrates. We present and evaluate a cap-based framework in which the network and clients cooperate to improve the overall Quality of Experience (QoE). First, to motivate the framework, we conduct a comprehensive study using the lab setup showing that a fixed rate cap comes with both benefits (e.g., data savings, improved stability and fairness) and drawbacks (e.g., higher startup times and slower recovery after stalls). To address the drawbacks while keeping the benefits, we then introduce and evaluate a framework that includes (i) buffer-aware rate caps in which the network temporarily boosts the rate cap of clients during video startup and under low buffer conditions, and (ii) boost-aware client-side adaptation algorithms that optimize the bitrate selection during the boost periods. Combined with information sharing between the network and clients, these mechanisms are shown to improve QoE, while reducing wasted bandwidth.

We present FlexStream, a programmable framework realized by implementing Software-Defined Networking (SDN) functionality on end devices. FlexStream exploits the benefits of both centralized and distributed components to achieve dynamic management of end devices, as required and in accordance with specified policies. We evaluate FlexStream on one example use case -- the adaptive video streaming, where bandwidth control is employed to drive selection of video bitrates, improve stability and increase robustness against background traffic. When applied to competing streaming clients, FlexStream reduces bitrate switching by 81%, stall duration by 92%, and startup delay by 44%, while improving fairness among players. In addition, we report the first implementation of SDN-based control in Android devices running in real Wi-Fi and live cellular networks.

Video streaming traffic is rapidly growing in mobile networks. Mobile Network Operators (MNOs) are expected to keep up with this growing demand, while maintaining a high video Quality of Experience (QoE). This makes it critical for MNOs to have a solid understanding of users' video QoE with a goal to help with network planning, provisioning and traffic management. However, designing a system to measure video QoE has several challenges: i) large scale of video traffic data and diversity of video streaming services, ii) cross-layer constraints due to complex cellular network architecture, and iii) extracting QoE metrics from network traffic. In this paper, we present VideoNOC, a prototype of a flexible and scalable platform to infer objective video QoE metrics (e.g., bitrate, rebuffering) for MNOs. We describe the design and architecture of VideoNOC, and outline the methodology to generate a novel data source for fine-grained video QoE monitoring. We then demonstrate some of the use cases of such a monitoring system. VideoNOC reveals video demand across the entire network, provides valuable insights on a number of design choices by content providers (e.g., OS-dependent performance, video player parameters like buffer size, range of encoding bitrates, etc.) and helps analyze the impact of network conditions on video QoE (e.g., mobility and high demand).

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error.

Connected cars are a rapidly growing segment of Internet of Things (IoT). While they already use cellular networks to support emergency response, in-car WiFi hotspots and infotainment, there is also a push towards updating their firmware over-the-air (FOTA). With millions of connected cars expected to be deployed over the next several years, and more importantly persist in the network for a long time, it is important to understand their behavior, usage patterns, and impact --- both in terms of their experience, as well as other users. Using one million connected cars on a production cellular network, we conduct network-scale measurements of over one billion radio connections to understand various aspects including their spatial and temporal connectivity patterns, the network conditions they face, use and handovers across various radio frequencies and mobility patterns. Our measurement study reveals that connected cars have distinct sets of characteristics, including those similar to regular smartphones (e.g. overall diurnal pattern), those similar to IoT devices (e.g. mostly short network sessions), but also some that belong to neither type (e.g. high mobility). These insights are invaluable in understanding and modeling connected cars in a cellular network and in designing strategies to manage their data demand.

Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information.

HTTP-based adaptive protocols dominate today's video streaming over the Internet, and operate using multiple quality levels that video players request one segment at a time. Despite their popularity, studies have shown that performance of video streams still suffers from stalls, quality switches and startup delay. In wireless networks, it is well-known that high variability in network bandwidth affects video streaming. MultiPath TCP (MPTCP) is an emerging paradigm that could offer significant benefits to video streaming by combining bandwidth on multiple network interfaces, in particular for mobile devices that typically support both WiFi and cellular networks. In this paper, we explore whether MPTCP always benefits mobile video streaming. Our experimental study on video streaming using two wireless interfaces yields mixed results. While beneficial to user experience under ample and stable bandwidth, MPTCP may not offer any advantage under some network conditions. We find that when additional bandwidth on the secondary path is not sufficient to sustain an upgrade in video quality, it is generally better not to use MPTCP. We also identify that MPTCP can harm user experience when an unstable secondary path is added to the stable primary path.

A primary design decision in HTTP/2, the successor of HTTP/1.1, is object multiplexing. While multiplexing improves web performance in many scenarios, it still has several drawbacks due to complex cross-layer interactions. In this paper, we propose a novel multiplexing architecture called TM3 that overcomes many of these limitations. TM3 strategically leverages multiple concurrent multiplexing pipes in a transparent manner, and eliminates various types of head-of-line blocking that can severely impact user experience. TM3 works beyond HTTP over TCP and applies to a wide range of application and transport protocols. Extensive evaluations on LTE and wired networks show that TM3 substantially improves performance e.g., reduces web page load time by an average of 24% compared to SPDY, which is the basis for HTTP/2. For lossy links and concurrent transfers, the improvements are more pronounced: compared to SPDY, TM3 achieves up to 42% of average PLT reduction under losses and up to 90% if concurrent transfers exist.

Recent studies have shown that web browsing is one of the most prominent cellular applications. It is therefore important for cellular network operators to understand how radio network characteristics (such as signal strength, handovers, load, etc.) influence users' web browsing Quality-of-Experience (web QoE). Understanding the relationship between web QoE and network characteristics is a pre-requisite for cellular network operators to detect when and where degraded network conditions actually impact web QoE. Unfortunately, cellular network operators do not have access to detailed server-side or client-side logs to directly measure web QoE metrics, such as abandonment rate and session length. In this paper, we first devise a machine-learning-based mechanism to infer web QoE metrics from network traces accurately. We then present a large-scale study characterizing the impact of network characteristics on web QoE using a month-long anonymized dataset collected from a major cellular network provider. Our results show that improving signal-to-noise ratio, decreasing load and reducing handovers can improve user experience. We find that web QoE is very sensitive to inter-radio-access-technology (IRAT) handovers. We further find that higher radio data link rate does not necessarily lead to better web QoE. Since many network characteristics are interrelated, we also use machine learning to accurately model the influence of radio network characteristics on user experience metrics. This model can be used by cellular network operators to prioritize the improvement of network factors that most influence web QoE.

Redundant Traffic Elimination (RTE) detects and removes repeated chunks of data across network flows, protocols, and applications, with the purpose of reducing bandwidth usage. In this paper, we explore the effectiveness of RTE in WLAN, compare it to RTE in Ethernet, and investigate specific issues affecting RTE in WLAN. Our results show that applying RTE to WLAN links is promising and can potentially yield high bandwidth savings, although RTE is not as effective in WLAN as in wired networks. However, to exploit the full potential of RTE, it is necessary to deal with specific challenges, such as longer headers, control and management frames, retransmissions, and dropped frames. We find that including parts of MAC headers in RTE can increase overall bandwidth savings by up to 53% in a public WLAN. To handle dropped frames, which can severely compromise the effectiveness of RTE, we make a case for MAC-layer RTE, which detects frame loss at the sender. This preserves 23% more savings than a previous approach. However, frame retransmissions generate additional traffic at MAC layer, which reduces the effectiveness of RTE in general case.

Cellular network operators have a compelling interest to monitor HTTP transaction latency because it is an important component of the user experience. Existing techniques to monitor latency require active probing or use passive analysis to estimate round trip time (RTT). Unfortunately, it is impractical to use active probing to monitor entire cellular networks, and RTT is only one component of HTTP latency in cellular networks. This paper presents a new passive technique to estimate HTTP transaction latency that overcomes the scaling and completeness limitations of prior approaches. We validate our technique in an operational cellular network and present results for traffic in the wild.

Protocol-independent redundant traffic elimination (RTE) is an "on the fly" method for detecting and removing redundant chunks of data from network-layer packets traversing a constrained link or path. Efficient algorithms are needed to sample data chunks and detect redundancy, so that RTE does not hinder network throughput. A recently proposed static algorithm samples chunks based on highly-redundant trigger bytes observed in data content. While this algorithm is fast, it requires pre-computed traffic information for the configuration of its static parameters, and it tends to either under-sample (reducing byte savings) or over-sample (increasing processing cost) on heterogeneous traffic. We propose a dynamic sampling algorithm for redundant content detection. Our algorithm is adaptive and self-configuring, and can precisely match the specified sampling rate. Furthermore, it offers byte savings comparable to the static algorithm, with very low additional processing overhead.

This paper describes the analysis of low-level measurements from a CDMA2000 1x cellular data network. The network traces record detailed information about wireless Internet packet data call activity on the network, including mobile station identity, call initiation, burst behaviour, supplementary channel usage, soft handoffs, and call termination. The analysis in this paper focuses on one continuous week-long trace data set, representative of cellular data network activity. The results from the analysis illustrate the burstiness of the packet call arrival process and the diurnal patterns of cellular data users. The results also characterize the activity per cell site, activity per user, data burst activity, user mobility, and the density of cellular network coverage. Several observations reinforce known results about heavytailed properties in wired Internet traffic, while others show interesting differences in wireless versus wireline traffic

Project JXTA is an open-source effort to specify the standard protocols for peer-to-peer communication and collaboration. We propose a JXTA performance model and present results obtained by benchmarking the JXTA 1.0 reference implementation in Java. We focus on the performance evaluation of typical peer operations and consequences for the peer network, the user and the developer. The important trade-off between peer startup latency and the maintenance of the local cache is shown and discussed. The throughput limits of pipes, the core JXTA communication concept, are also measured in a LAN environment for smooth and bursty traffic. The results indicate that the limiting factor for reliable throughput is the number of messages rather than size in bytes, as well as that small JXTA messages carry an excessive overhead of control data. Important performance issues and trade-offs are identified and explored, as a basis for the formulation of guidelines for system designers and simulation-based research of JXTA networks.

Decentralized file-sharing systems like Napster and Gnutella have popularized the peer-to-peer approach, which emphasizes the use of distributed resources in a decentralized manner. Peer-to-peer (P2P) systems are a relatively new addition to the large area of distributed systems. Their emphasis on sharing distributed resources, self-organization and use of discovery mechanisms sets them apart from other forms of distributed computing. Avoiding centralized components, and extensive resource/service sharing allows P2P systems to outperform other forms of distributed systems with regards to scalability and robustness due to load distribution and the avoidance of bottlenecks and single points of failure. This paper has two aims; firstly to report on the use of JXTA in converting a server-centric legacy forum system into a P2P system. It also attempts to encourage others in redesigning existing client-server systems into P2P applications as a way of to better understand and evaluate the costs and benefits of this technology.