Optimizing quality of experience (QoE) for live video streaming (LVS) remains a long-standing challenge for content delivery network (CDN) providers. Today’s CDNs predominantly employ static congestion control (CC) configurations, yet the heterogeneity of LVS scenarios undermines the efficacy of a uniform CC solution applicable to all users, as evidenced by our production measurements. While learning-based CC approaches show promise, they often suffer from limited generalizability, non-transparent design, and high computational overhead, struggling in large-scale CDNs. In this paper, we propose BIFROST, a new CC architecture grounded in a concerndecoupling paradigm. BIFROST separates fixed control logic from scenario-specific parameter optimization, enabling CDN systems to adapt dynamically to diverse LVS scenarios. To materialize it at CDN scale, BIFROST introduces a bilateral collaboration mechanism that identifies the LVS scenarios of each session by extracting client-side characteristics from viewing requests. It further employs offline model-based optimization to derive more effective control parameters for each scenario. We have deployed BIFROST on Alibaba Cloud’s production CDN for nearly a year, serving a commercial LVS application. BIFROST has markedly improved QoE metrics by 2.7% to 32.1%, reinforcing the competitive edge in the CDN market. We also share our experiences and lessons learned from its large-scale deployment.

The escalating need for live video streaming has emerged as a significant catalyst for the business expansion of today’s content delivery networks (CDN). Selecting the right CDN live streaming architecture is fundamentally important in achieving the objective of enhancing users’ quality of experience (QoE) while reducing bandwidth costs. Regrettably, a limited number of studies have been conducted to systematically measure and compare the current typical solutions at production scale. Consequently, the performance and costs of different streaming architectures remain myths. This paper aims to address the existing research gap by undertaking a large-scale measurement study of three representative CDN live streaming architectures, defined by streaming protocol and overlay topology choices, currently running on Alibaba Cloud’s production video delivery network. By analyzing the results of over 500 million video plays over two months on a large live streaming platform hosted on Alibaba Cloud’s CDN, we reveal the impact of architectural compositions and operational factors on live streaming performance and bandwidth costs. In particular, our study reveals the trade-offs between QoE metrics and bandwidth costs for operational streaming architectures. Drawing upon the insights of this study, we further develop and deploy pragmatic strategies that yield remarkable real-world impact—our design saves over 17% bandwidth costs while maintaining the QoE.

Live video streaming has come to dominate today’s Internet traffic. Content Delivery Network (CDN) providers, responsible for hosting outsourced live streaming services, are now striving to ensure an enhanced quality of experience (QoE) to meet the ever-increasing user expectations. Existing congestion control (CC) schemes in the kernel, however, suffer from unsatisfactory performance for live video delivery due to disparities in traffic characteristics and differentiated optimization goals between generic traffic and live video traffic. In this paper, we propose XCC, a streaming context-aware CC approach that helps achieve better QoE for the live streaming services from CDN provider. The core of XCC is to adaptively coordinate the transmission strategy and frame rate through a cross-layer feedback framework, responding to the fluctuating traffic dynamics and network conditions in the short term. Further, XCC matches the long-term traffic characteristics (i.e., two-stage delivery mode) by employing a task-specific state transition mechanism as the underlying TCP. XCC has been implemented in the Linux kernel’s TCP stack and media engine and has been fully deployed in Alibaba Cloud’s production service. Evaluation in experimental environments and A/B testing serving tens of millions of sessions demonstrate that XCC is competitive in streaming delay against the most prevalent TCP in today’s Operating Systems, while reducing startup delay by 9.9%, stall time by 36.4%, and stall frequency by 42.5% on average in deployment.

Video service providers have deployed dynamic video bitrate adaptation services to fulfill user demands for higher video quality. However, fluctuations and instability of network conditions inhibit the performance promotion of adaptive bitrate (ABR) algorithms. Existing rule-based approaches fail to guarantee accurate throughput estimates, and learning-based algorithms are considerably sensitive to the variability of network. Therefore, how to gain effective and stable throughput estimates has become one of the critical challenges to further enhancing ABR methods. To eliminate this concern, we propose PRIOR, an ABR algorithm that fuses an effective throughput prediction module and a state-of-the-art multi-agent reinforcement learning method to provide a high quality of experience (QoE). PRIOR aims to maximize the QoE metric by straightforwardly utilizing accurate throughput estimates rather than past throughput measurements. Specifically, PRIOR employs a light-weighted prediction module with attention mechanism to obtain effective future throughput. Considering the excellent features introduced by the HTTP/3 protocol, we apply PRIOR to trace-driven simulations and real-world scenarios over HTTP/1.1 and HTTP/3. Trace-driven emulation illustrates that PRIOR outperforms existing ABR schemes over HTTP/1.1 and HTTP/3, and our prediction module can also reinforce the performance of other ABR algorithms. Extensive results on real-world evaluation demonstrate the superiority of PRIOR over existing state-of-the-art ABR schemes.