Optimal QoE-Fair Resource Allocation in Multipath Video Delivery Network

Abstract

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters.