On-Demand Video-Streaming Quality of Experience Maximization in Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) has recently emerged as a promising paradigm to enhance mobile networks' performance by providing cloud-computing capabilities to the edge of the Radio Access Network (RAN) with the deployment of MEC servers right at the Base Stations (BSs). Meanwhile, in-network caching and video transcoding have become important complementary technologies to lower network cost and to enhance Quality of Experience (QoE) for video-streaming users. In this paper, we aim at optimizing the QoE for dynamic adaptive video streaming by taking into account the Distortion Rate (DR) characteristics of videos and the coordination among MEC servers. Specifically, a novel Video-streaming QoE Maximization (VQM) problem is cast as a Mixed-Integer Nonlinear Program (MINLP) that jointly determines the integer video resolution levels and video transmission data rates. Due to the challenging combinatorial and non-convex nature of this problem, the Dual-Decomposition Method (DDM) is employed to decouple the original problem into two tractable subproblems, which can be solved efficiently using standard optimization solvers. Realtime experiments on a wireless video streaming testbed have been performed on a FDD-downlink LTE emulation system to characterize the performance and computing resource consumption of the MEC server under various realistic conditions. Emulation results of the proposed strategy show significant improvement in terms of users' QoE over traditional approaches.