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Recent advances in quality adaptation algorithms leave adaptive bitrate (ABR)streaming architectures at a crossroads: When determining the sustainable videoquality one may either rely on the information gathered at the client vantagepoint or on server and network assistance. The fundamental problem here is todetermine how valuable either information is for the adaptation decision. Thisproblem becomes particularly hard in future Internet settings such as NamedData 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 QoEmaximization, which we formalize as a decision problem under uncertainty, andfor which we contribute a sparse Bayesian contextual bandit algorithm denotedCBA. This allows taking high-dimensional streaming context information,including client-measured variables and network assistance, to find online themost valuable information for the quality adaptation. Since sparse Bayesianestimation is computationally expensive, we develop a fast new inference schemeto support online video adaptation. We perform an extensive evaluation of ouradaptation algorithm in the particularly challenging setting of NDN, where weuse an emulation testbed to demonstrate the efficacy of CBA compared tostate-of-the-art algorithms.

CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming (Extended Version)