QoE-Aware Collaborative Edge Caching and Computing for Adaptive Video Streaming

Abstract

By encoding the video into different bitrate versions, dynamic adaptive streaming over HTTP (DASH) demonstrates its unique advantages in providing flexible bitrate adaption service in dynamic environments. But, the price is that the amount of video data is dramatically increased. The interaction of massive video data tends to exacerbate the network congestion and degrades the quality of experience (QoE) of users. Edge caching and mobile edge computing (MEC) have been adopted to solve this problem and enhance the QoE. But it is still difficult because of the highly coupled nature of caching and computing, which makes it extremely challenging to coordinate them across multiple edge nodes. To address the problem, this paper devotes itself to investigating collaborative edge caching and computing to maximize QoE for adaptive video streaming. In doing so, an optimization problem is formulated by jointly designing the caching, computing and user bitrate adaption, which turns out to be an integer nonlinear programming (INLP) problem and is NP-hard in strong sense. To solve it, we include caching placement, joint computing and bitrate adaption into a two-stage optimization framework. Specifically, considering the fact that the caching placement is implemented at a relatively long timescale, the caching problem is reformulated based on the statistics of user requests. The reformulated problem is a multiple-choice knapsack problem (MCKP), which is solved by Lagrange dual method after relaxation. The joint computing and bitrate adaption problem is transformed into Markov decision process (MDP) problem, and is solved by deep deterministic policy gradient (DDPG) algorithm. Simulation results validate that the proposed scheme can significantly improve QoE when compared with state-of-the-art baselines.