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.

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.

Small unmanned air vehicles (UAVs) have many advantages, including low cost and flexible deployment. And they play an important role to collect the sensing data of Internet of Things (IoT). However, limited by the load capability, it is a big challenge for them to perform long-term, large range, or far distance tasks. In order to tackle these challenges, we propose to use assembly UAVs, in which we can jointly optimize the resource management, especially, the energy resource. The system model, energy cyclic cooperation, and one of the typical applications based on assembly UAVs are introduced. The energy cooperation problems are formulated and an in-air replenishing strategy (IA-RS) is proposed. Simulations show that the performances of the proposed IA-RS outperform those of the traditional on-ground replenishing strategy (OG-RS). The working time of task UAV (UAV-T) could reduce 8.3%–19.5%, and the freshness of the collected data and the collecting efficiency of the UAV-T can be improved. We also optimize the path of the replenishing UAVs (UAV-Rs). Simulations show that the proposed reinforcement learning (RL) algorithm has the best performances and acceptable complexity. Consequently, the efficiency of the IoT data collection task is improved by the proposed assembly UAVs.

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.

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.

Inspired by the remarkable advances in video analytics, research teams are stepping towards a greater ambition - movie understanding. However, compared to those activity videos in conventional datasets, movies are significantly different. Generally, movies are much longer and consist of much richer temporal structures. More importantly, the interactions among characters play a central role in expressing the underlying story. To facilitate the efforts along this direction, we construct a dataset called Movie Synopses Associations (MSA) over 327 movies, which provides a synopsis for each movie, together with annotated associations between synopsis paragraphs and movie segments. On top of this dataset, we develop a framework to perform matching between movie segments and synopsis paragraphs. This framework integrates different aspects of a movie, including event dynamics and character interactions, and allows them to be matched with parsed paragraphs, based on a graph-based formulation. Our study shows that the proposed framework remarkably improves the matching accuracy over conventional feature-based methods. It also reveals the importance of narrative structures and character interactions in movie understanding. Dataset and code are available at: https://ycxioooong.github.io/projects/moviesyn.