Context-Aware Cross-Layer Congestion Control for Large-Scale Live Streaming

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.