Cross-layer Network Bandwidth Estimation for Low-latency Live ABR Streaming

Low-latency live (LLL) adaptive bitrate (ABR) streaming relies critically on accurate bandwidth estimation to react to dynamic network conditions. While existing studies have proposed bandwidth estimation techniques for LLL streaming, these approaches are at the application level, and their accuracy is limited by the distorted timing information observed at the application level. In this paper, we propose a novel cross-layer approach that uses coarse-grained application-level semantics and fine-grained kernel-level packet capture to obtain accurate bandwidth estimation. We incorporate this technique in three popular open-source ABR players and show that it provides significantly more accurate bandwidth estimation than the state-of-the-art application-level approaches. In addition, the more accurate bandwidth estimation leads to better bandwidth prediction, which we show can lead to significantly better quality of experience (QoE) for end users.