CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming (Extended Version)
This work addresses the optimization of video streaming quality for users in future Internet architectures, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of determining the most valuable information for adaptive bitrate (ABR) video streaming quality adaptation, particularly in challenging settings like Named Data Networking (NDN), by proposing CBA, a sparse Bayesian contextual bandit algorithm that maximizes Quality of Experience (QoE). It demonstrates CBA's efficacy compared to state-of-the-art algorithms through extensive evaluation on an emulation testbed.
Recent advances in quality adaptation algorithms leave adaptive bitrate (ABR) streaming architectures at a crossroads: When determining the sustainable video quality one may either rely on the information gathered at the client vantage point or on server and network assistance. The fundamental problem here is to determine how valuable either information is for the adaptation decision. This problem becomes particularly hard in future Internet settings such as Named Data Networking (NDN) where the notion of a network connection does not exist. In this paper, we provide a fresh view on ABR quality adaptation for QoE maximization, which we formalize as a decision problem under uncertainty, and for which we contribute a sparse Bayesian contextual bandit algorithm denoted CBA. This allows taking high-dimensional streaming context information, including client-measured variables and network assistance, to find online the most valuable information for the quality adaptation. Since sparse Bayesian estimation is computationally expensive, we develop a fast new inference scheme to support online video adaptation. We perform an extensive evaluation of our adaptation algorithm in the particularly challenging setting of NDN, where we use an emulation testbed to demonstrate the efficacy of CBA compared to state-of-the-art algorithms.