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.