Heavy-traffic analysis of QoE optimality for on-demand video streams over fading channels

This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.