Restless Video Bandits: Optimal SVC Streaming in a Multi-user Wireless Network

TL;DR

This paper introduces a scheduling mechanism for scalable video delivery in wireless networks that optimizes user experience by considering Quality Adaptation (QA) logic, using Restless Bandits and Semi Markov Decision Processes, with heuristic algorithms for practical implementation.

Contribution

The paper proposes a QA-aware scheduling approach based on Restless Bandits that improves network utilization without requiring centralized QA coordination.

Findings

QA-Aware strategy doubles network utilization over baseline algorithms.

Heuristic algorithms enable practical implementation of the optimal scheduling.

Testbed results validate the effectiveness of the proposed schemes.

Abstract

In this paper, we consider the problem of optimal scalable video delivery to mobile users in wireless networks given arbitrary Quality Adaptation (QA) mechanisms. In current practical systems, QA and scheduling are performed independently by the content provider and network operator, respectively. While most research has been focused on jointly optimizing these two tasks, the high complexity that comes with a joint approach makes the implementation impractical. Therefore, we present a scheduling mechanism that takes the QA logic of each user as input and optimizes the scheduling accordingly. Hence, there is no need for centralized QA and cross-layer interactions are minimized. We model the QA-adaptive scheduling and the jointly optimal problem as a Restless Bandit and a Multi-user Semi Markov Decision Process, respectively in order to compare the loss incurred by not employing a jointly optimal scheme. We then present heuristic algorithms in order to achieve the optimal outcome of the Restless Bandit solution assuming the base station has knowledge of the underlying quality adaptation of each user (QA-Aware). We also present a simplified heuristic without the need for any higher layer knowledge at the base station (QA-Blind). We show that our QA-Aware strategy can achieve up to two times improvement in user network utilization compared to popular baseline algorithms such as Proportional Fairness. We also provide a testbed implementation of the QA-Blind scheme in order to compare it with baseline algorithms in a real network setting.