Short-video services have seen explosive growth in recent years. Streaming over mobile networks is inherently challenging due to the latter's bandwidth fluctuations, motivating researchers to develop many sophisticated adaptive bitrate (ABR) algorithms to compensate. While ABR, together with prefetching, has been proposed for playlist streaming, its application to non-playlist streaming has received little attention. This work fills this gap by first exploring the efficacy of directly applying ABR to non-playlist streaming. Observing their limitations motivates the development of a new class of inter-stream bitrate adaptation (ISA) algorithms. Unlike ABR, ISA adapts bitrate on a per-video basis, which is not only simpler to implement and deploy but can even outperform ABR algorithms by up to 66.71% across a wide range of networks. Moreover, ISA and ABR are complementary such that they can be combined into Integrated Bitrate Adaptation (IBA) algorithms to raise performance gains further by up to 77.03%. In addition, this work develops a novel adaptive rebuffering duration (ARD) algorithm specifically designed for frame-based playback common in short-video services to further improve their performance under challenging network conditions. Together, ISA and ARD offer a new set of tools with progressive complexity-performance tradeoffs for enhancing the performance of short-video services.

Short video applications such as TikTok, Douyin, and Kwai have experienced significant popularity in recent years. However, the quality of experience (QoE) provided by short video streaming services still falls short of expectations. As a leading provider of short video services with proprietary video players and content delivery network (CDN) capabilities, we are in a unique position to optimize the QoE of these services. In this study, we present our pilot investigation into congestion control performance optimization for short video services by leveraging collaboration between user-end video players and edge servers. Based on a comprehensive measurement study of network characteristics from production networks and incorporating feedback from video players, we developed an optimized congestion control algorithm called BBR-E2E. We deployed BBR-E2E in our production network and conducted a large-scale A/B testing across the country, involving trillions of video sessions over a three-month period in China. Overall, we observed a 1.6% reduction in rebuffering duration and a 6.2% decrease in rebuffering count. At the provincial level11A province in China is similar to a state in the USA., the improvements were even more substantial, with up to a 7.8% reduction in rebuffering duration and a 13.7% decrease in rebuffering count.

With the rapid deployment of LTE/5 G services, mobile subscribers now have access to high-speed services approaching Gbps. However, most mobile data plans have data quota from a few GBs up, beyond which the subscriber will be restricted to much lower bandwidth (e.g., 1 Mbps)-rate-limited service. Rate limiting not only poses a significant challenge to service providers, as it is often mistaken for network problems, triggering false alarms at the providers, but may also cause significant performance anomalies at the application layer and transport layer. This work tackles two central problems in mobile network rate-limiting, namely rate-limiting classification and parameter estimation, through a novel model-based online rate-limiter (MODRL) detector that can detect the presence of rate limiting and estimate its parameters passively from transport layer ACK. Experiments in controlled network testbed and production 4 G/5 G mobile networks show that MODRL can achieve remarkably high and consistent classification accuracy across a wide range of networks. Preliminary results from integrating MODRL into adaptive video streaming and QUIC transport demonstrate that it can effectively eliminate the performance anomalies caused by rate limiting, and open new avenues to further optimize protocol performance over rate-limited mobile networks.

With the rapid advances in high-speed mobile networks such as 5G, Wi-Fi 6, and the upcoming 6G and Wi-Fi 7, streaming live video has become ubiquitous for mobile users. However, live video is susceptible to short-term network condition fluctuations which could lead to video stalls. Our investigations revealed that a substantial portion of such fluctuations were in fact caused by RTT spikes that were not congestion-related. These often confuse the transport protocol into dropping the transmission rate significantly, resulting in video stalls. This motivated us to develop a novel scheme called CWnd-loan to reduce the sender's CWnd-limited idle time during RTT spikes. We applied CWnd-loan to the QUIC protocol with BBR/CUBIC congestion control and strategically deployed it in a tier-1 live video service. The results show that CWnd-loan can effectively reduce sender CWnd-limited idle time by up to 18%, consequently reducing the duration and number of live video stalls by as much as 8.9% and 10.6%. Furthermore, CWnd-loan can also reduce the first-frame time and the playback failure rate by up to 3.2% and 2.7%, respectively. CWnd-loan is designed to complement existing congestion control algorithms and thus could potentially be applied to current as well as future TCP/QUIC designs to tackle RTT spikes commonly found across mobile and wireless networks.

In spite of the advances in mobile networks, most mobile data plans impose a fixed monthly data quota, beyond which the attainable bandwidth is explicitly limited to a much lower data rate. This rate-limited behavior could degrade TCP performances significantly, as confirmed by a major service provider who observed strong correlation between high packet loss rate and mobile rate limiting. The high packet loss translates directly into increased bandwidth cost which is significant in a large-scale service. This work investigates this problem in two steps. First, we establish the link between the high loss rate observed and mobile network rate limiting through experiments in both controlled testbed and production mobile networks. The results revealed that packet loss can and does increase dramatically in rate-limited mobile networks. This affects both TCP Cubic and BBR, the two most widely deployed TCP implementations. BBR, in particular, was impacted far more significantly, resulting in packet loss rates exceeding 40% in some cases. Second, we analyzed BBR's operations under rate limiting to uncover the causes and developed new mechanisms - mBBR, to improve its performance. Experimental results show that mBBR can reduce BBR's packet loss rate by up to 88%, thereby saving substantial bandwidth costs incurred in retransmitting lost packets when the user is under rate limiting.

Learning-based Internet congestion control algorithms have attracted much attention due to their potential performance improvement over traditional algorithms. However, such performance improvement is usually at the expense of black-box design and high computational overhead, which prevent them from large-scale deployment over production networks. To address this problem, we propose a novel Internet congestion control algorithm called Gemini. It contains a parameterized congestion control module, which is white-box designed with low computational overhead, and an online parameter optimization module, which serves to adapt the parameterized congestion control module to different networks for higher transmission performance. Extensive trace-driven emulations reveal Gemini achieves better balances between delay and throughput than state-of-the-art algorithms. Moreover, we successfully deploy Gemini over production networks. The evaluation results show that the average throughput of Gemini is 5% higher than that of Cubic (4% higher than that of BBR) over a mobile application downloading service and 61% higher than that of Cubic (33% higher than that of BBR) over a commercial network speed-test benchmarking service.

Short-video sharing services have seen explosive growth in recent years. Compared to conventional video sharing platforms, these have very different characteristics which are far from well-understood. This work aims at filling the gap by measuring and analyzing detailedapplication-levelperformance data from a top-10 short video service in China. The application-level data offered detailed and rare insights into many performance metrics of the service, which are otherwise inaccessible to external measurements. The service has a scale of over one billion daily views just for the mobile and wireless segments of the service. Our datasets covered over 22 billion video playbacks, over 100 million video files, served by over 5,000 servers to users across 35 provinces and 13 ISPs in China. We analyzed three aspects of the service: (a) video content characteristics; (b) network analytics; and (c) video streaming analytics. Our results revealed significant differences from conventional video-sharing platforms. These findings will have implications for system designs at all levels. The data also enabled us to conduct an indirect network performance measurement of mobile and wireless network services across China,as experiencedby the service. These results offer rare insights into mobile and wireless networks' real-world performance in a large country.

Video streaming has grown tremendously in recent years and it is now one of the main applications on the Internet. Due to the networks' inherent bandwidth fluctuations, various rate-adaptive streaming algorithms have been developed to compensate for such fluctuations to improve Quality-of-Experience (QoE). However, in practice, the preference for QoE typically differs significantly across different viewers and there is no systematic way so far to comprehensively incorporate different sets of conflicting QoE objectives into the algorithm design. Thus, it is not surprising that the QoE performance achieved by the existing algorithms is in fact far from optimal. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) that can maximize the QoE under any preference through automatically tuning the adaptation logic of the streaming algorithms. Evaluation results show that the QoE achieved by PSQA is substantially better than the existing approaches and in some scenarios even close to optimal. Moreover, PSQA can be readily implemented into real streaming platforms, offering a practical and reliable solution for high-performance streaming services.

With the progressive deployment of 5G networks around the world, mobile networks are entering a new era where bandwidth will be breaking through the Gbps barrier. In this work, we investigate the performance of current TCP designs in such high-bandwidth networks, demonstrating the potential bottleneck due to TCP’s Slow-Start mechanism which is an integral component in most TCP designs. For example, transferring a file of 1 MB size in a first-generation 5G network using Linux’s default TCP-Cubic and Google’s TCP-BBR resulted in average throughputs of 18.2 Mbps and 32.8 Mbps, respectively. Compared to the mean available bandwidth of 180 Mbps, the gap is significant. To tackle this problem, we developed an enhanced Stateful-TCP technique to transform BBR into a new S-BBR to accelerate its startup performance to narrow the gap. Results from trace-driven emulated 5G network experiments show that S-BBR could improve BBR’s throughput performance by 50% to 100% while maintaining similar delay performance. This is further validated by an independent competitive benchmark using over 500 clients where S-BBR raised BBR’s throughput by 69%. S-BBR is sender-based and thus can be readily deployed in Internet servers without any requirements from the client side, it retains BBR’s desirable features and so offers a promising solution to enhance mobile applications’ performance in the emerging high-bandwidth mobile and wireless networks.

Fueled by the growth of 3G/4G mobile networks, mobile video streaming has become one of the main applications in the mobile Internet. Due to mobile networks' inherent bandwidth fluctuations, the industry as well as researchers have developed many adaptive streaming algorithms to compensate for such fluctuations to improve streaming performance. Given the wide range of network settings, it is not surprising that existing algorithms can and do perform differently across different network and system conditions. This work breaks away from the conventional one-size-fits-all approach to designing adaptive streaming systems by developing a new framework called PSRA where past throughput trace data - captured as a by-product of streaming, are analyzed to construct a statistical model to automatically tune the adaptation algorithm for future streaming sessions according to the underlying network and system configurations. Compared to existing approaches, the PSRA-optimized streaming algorithm can achieve predictable, consistent, and controllable streaming performance across a wide-range of network and system configurations. Moreover, PSRA offers to service provider a new tool to precisely control the tradeoff between video quality and streaming performance. Results from extensive trace-driven simulations as well as experiments verified PSRA's performance under real-world mobile network and system configurations.

Mobile video streaming is one of the fastest growing applications in the mobile Internet. Nevertheless, delivering high-quality streaming video over mobile networks remains a challenge. Researchers have since developed various novel streaming algorithms such as rate-adaptive streaming to improve the performance of mobile streaming services. However, selection or optimization of streaming algorithms is far from trivial and there is no systematic way to incorporate the tradeoffs between various performance metrics. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) to automatically tune any streaming algorithms to maximize a given quality-of-experience (QoE) objective. We show that PSQA not only can be applied to optimize the performance of existing streaming algorithms, but also opens a new way for the exploration of new adaptive video streaming protocols and QoE metrics. Simulation results based on real network throughput traces show that PSQA can optimize existing and new streaming algorithms to achieve QoE that is remarkably close to the optimal achieved using brute-force method ex post facto.

Mobile video streaming services are ubiquitous today and yet their real-world performance is still largely inconsistent, unpredictable, and uncontrollable, primarily due to mobile networks' inherent bandwidth fluctuations at both short and long timescales. With the rapid emergence of paid streaming services, picture and streaming qualities can no longer be an afterthought. This work develops a novel framework called Variable-Bitrate Post-Streaming Rate Analysis (VBR-PSRA) that, for the first time, enables service providers and mobile operators to provision higher-quality VBR-encoded video streaming services with consistent, predictable, and controllable performance. The VBR-PSRA framework exploits past throughput trace data collected during actual streaming sessions to construct a statistical model that captures and quantifies the relation between recent throughput data, video quality choice, video bitrate variations, and streaming performance. It allows the service provider to set a target streaming performance in terms of playback rebuffering probability where it will then automatically select the best video quality that can be streamed. Extensive simulations using trace data obtained from production 3G/HSPA networks in three different locations showed that the proposed VBR-PSRA framework can achieve actual streaming performances which are remarkably close to the target.

Bandwidth-sensitive applications such as adaptive video streaming rely on accurate prediction of future network throughput to enable them to react to and compensate for the rapidly fluctuating bandwidth often found in mobile networks. Researchers have developed various prediction algorithms in the literature of which many have been employed in real-world applications. However, there is a lack of systematic study on the comparative performance of the existing prediction algorithms in the context of mobile networks. This work addresses this void by conducting a systematic performance comparison of 7 prediction algorithms, and analyzes their characteristics when applied to the prediction of TCP throughput in mobile networks. The performance results are obtained from extensive trace-driven simulations where the throughput trace data were captured in production 3G/HSPA mobile networks in 3 locations over a period of 9 months and hence offer a good representation of the prediction algorithms' real- world performance. Furthermore, we applied the theory of differential entropy in information theory to obtain an estimated lower bound on throughput prediction errors which, for the first time, enables one to evaluate the absolute performance of these prediction algorithms. The results revealed that more complex algorithms are not necessarily better, and there exists a specific range of operating parameters where predictions are generally more accurate.

Mobile video streaming has become a mainstream application due to vastly improved smartphone hardware and mobile network capacity in recent years. Nevertheless, mobile video streaming remains challenging in practice due to mobile network's inherent bandwidth fluctuations. This work tackles two long-standing challenges in mobile video streaming, namely to provision streaming services with predictable streaming performance and guaranteed video quality. In contrast to existing approaches based on adaptive video streaming, we show that today's mobile networks, despite the seemingly random bandwidth fluctuations, do exhibit statistically significant correlations over short and long time-scales. By developing a new framework to correlate the statistical correlations between video bitrate, streaming performance, and startup delay, we show that it is both possible and practical to achieve the above two goals by adaptively configuring the startup delay. Trace-driven simulations based on bandwidth traces captured from production 3G networks show that the proposed framework can readily achieve both streaming performance and video quality guarantees in today's mobile networks.

Adaptive video streaming is an essential tool for improving the performance of video delivery over mobile networks. By dynamically switching between different bit-rate versions of the same video, adaptive video streaming can compensate for and adapt to the ever-changing network conditions inherent in today's 3G/4G networks. However, existing adaptive streaming algorithms, both academic and commercial ones, do not offer any prediction on the streaming performance of future streaming sessions, nor allow the content providers or users to explicitly control the tradeoff between streaming performance and video quality. This study tackles this fundamental challenge by developing a novel framework called Throughput-Differentiated-Rate-Adaptive Post-Streaming-Rate-Analysis (TDRA-PSRA) based on a new statistical model that directly relates past bandwidth statistics to future streaming performance. Extensive simulation results obtained from real-world mobile network trace data revealed three remarkable properties of TDRA-PSRA: (a) the actual average streaming performance is very close to the target set forth by the ICPs/users; (b) it enables the ICPs/users to control the tradeoff between streaming performance and video quality; (c) it offers a mean to directly control the frequency of bit-rate switches — a key factor to subjective video quality.

Much work has been done to improve the performance of video streaming over mobile data networks. The widespread adoption of HTTP/TCP for video streaming further complicates the problem as TCP's own dynamics add even more fluctuations to the already unpredictable network bandwidth. Not surprisingly, none of the existing video bit-rate selection algorithms can achieve consistent or predictable streaming performance in the presence of wide network bandwidth fluctuations. This work tackles this problem by developing a new model to capture the statistical correlations between streaming performance, video bit-rate selection, and past TCP throughput data. Based on a novel post-streaming rate analysis technique, the proposed model can be used to predict the streaming performance of future video sessions at any given video bit-rate choices. This not only enables content providers to control the quality-of-service for streaming users, but also to ensure consistent streaming performance for users with widely different access bandwidths.