Enhanced Bandwidth Measurement and Robust Rate Adaptation for Low-latency Live Streaming

Jiahui Chen, Yiding Yu, L Wang, Ying Chen, Tianchi Huang, Lifeng Sun

Abstract:

Low latency live streaming (LLLS) like LL-DASH
has significantly reduced the end-to-end latency via chunked
transfer encoding (CTE). However, LLLS also comes with more
challenges for adaptive bitrate (ABR) algorithms: (1) bandwidth
measurement is non-trivial and inaccurate due to the possible
idle time between chunks in CTE; (2) the various uncertainty in
LLLS such as fluctuating segment size further lead to inaccurate
buffer estimation, severely degrading ABR’s performance. In
this paper, we propose AAR which comprises two modules: (1)
accurate bandwidth measurement via server-side Flag parameter
to identify the burst chunks within a segment, which allows for
more consecutive valid HTTP chunks; (2) an LLLS tailored ABR
with a novel robust objective that maximizes the minimum quality
of experience (QoE) brought by the uncertainty. To obtain the
minimum QoE, we propose a theorem based on the upper bound
of download time estimation, which is backed up by theoretical
guarantees. To derive the maximum QoE, we propose a new
LLLS state evolution mechanism and apply Model Predictive
Controller (MPC) to search for optimal bitrates. Extensive real
world experiments demonstrate that AAR outperforms existing
baselines with 10%-80% measurement error reduction, and
QoE improves by 39%-104% throughout all considered network
conditions.