{"ID":3004906,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T10:38:01.117085634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03468","arxiv_id":"2606.03468","title":"When BBR Meets Live Streaming","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.","short_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...","url_abs":"https://arxiv.org/abs/2606.03468","url_pdf":"https://arxiv.org/pdf/2606.03468v1","authors":"[\"Xu Yan\",\"Tong Li\",\"Bo Wu\",\"Cheng Luo\",\"Jiuxiang Zhu\",\"Laizhong Cui\"]","published":"2026-06-02T10:46:17Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.MM\",\"cs.NI\"]","methods":"[]","has_code":false}