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

## TL;DR

This paper introduces CBA, a Bayesian contextual bandit algorithm for adaptive bitrate video streaming that optimally leverages client and network information to maximize QoE, especially in future network architectures like NDN.

## Contribution

We propose a novel sparse Bayesian contextual bandit algorithm, CBA, for ABR streaming that effectively utilizes high-dimensional context data and is computationally efficient for online adaptation.

## Key findings

- CBA outperforms existing algorithms in NDN emulation tests.
- The fast inference scheme enables real-time adaptation.
- CBA effectively balances client and network information for QoE maximization.

## Abstract

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.