  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.
