🤖 AI Summary
This work addresses the limitations of BBR in live streaming scenarios, where inaccurate bandwidth estimation often causes BBR to remain stuck in its startup phase, leading to self-inflicted packet loss and insufficient throughput during steady state. To overcome these challenges, the authors propose BBR-Copilot, the first BBR-augmented congestion control mechanism specifically designed for live video streaming. BBR-Copilot intelligently injects probing packets to actively generate precise bandwidth measurement samples, thereby enabling more accurate and timely decisions by BBR. A prototype implementation over QUIC demonstrates that BBR-Copilot significantly improves throughput and stability while effectively mitigating self-induced packet loss, thus overcoming BBR’s inherent limitations in dynamic live-streaming environments.
📝 Abstract
Recently, industrial pioneers like Amazon, Tencent, ByteDance, and Huawei have been adopting BBR as their congestion control algorithm for live-streaming applications, including TikTok Live. However, BBR, originally crafted for bulk data transmission, faces multiple challenges in live-streaming scenarios. In this paper, we first explore two key issues associated with BBR due to inaccurate bandwidth estimation in live-streaming scenarios: (i) BBR cannot easily exit its startup phase, resulting in a fierce self-inflicted loss. (ii) BBR sends data at a lower rate than the available bandwidth during its stable phase. We then propose BBR-Copilot, an auxiliary congestion control component that cooperates with BBR, making BBR better adapt to live-streaming scenarios. BBR-Copilot allows for proactively generating accurate bandwidth measurement samples by smartly creating and sending extra data. We implement the BBR-Copilot prototype upon QUIC and evaluate it via testbed. Experimental evaluation results show that BBR-Copilot effectively enhances BBR's performance in live-streaming scenarios.