Cross that boundary: Investigating the feasibility of cross-layer information sharing for enhancing ABR decision logic over QUIC

Abstract

With HTTP Adaptive Streaming (HAS), client-side Adaptive Bi-trate (ABR) algorithms drive the (quality-variant) scheduling and downloading of media segments. These ABR algorithms are implemented in the application layer and can therefore base their logic only on relatively coarse and/or inaccurate application-layer met-rics. The recently standardized QUIC transport protocol has many userspace implementations, which paves the way for cross-layer optimizations by exposing transport-layer metrics to application-layer algorithms. In this paper, we investigate whether the availability of fine-grained transport-level throughput metrics can positively impact the operation of ABR algorithms and hence the Quality of Experience (QoE) of HAS users in Video on Demand (VoD) settings. Our results show that QUIC-level throughput data can indeed aid ABR algorithms to more accurately predict playout buffer under-runs, which in turn allows the ABR logic to take reactive measures in a timely fashion such that playback stalls can be avoided under challenging network conditions. Overall, our work presents a step towards improving ABR operation via cross-layer data exchange.