Delay and Energy Minimization for Adaptive Video Streaming: A Joint Edge Caching, Computing and Power Allocation Approach

Adaptive bitrate streaming can confront dynamic environment and satisfy multifarious user requirements by encoding video files into different bitrate versions. It can significantly improve the user quality of experience (QoE). But at the same time, it can also lead to a further increase in video data volume. The interaction of massive video data tends to aggravate network congestion and increase transmission energy, which triggers new challenges for wireless networks in terms of delivery delay and energy consumption. In this paper, taking different video sizes and playback rate requirements into account, we aim to minimize the weighted sum of delivery delay and energy consumption for adaptive video streaming. The problem is formulated by jointly optimizing caching, computing and power allocation, which turns out to be a mixed integer nonlinear programming (MINLP) problem. To tackle it, a joint caching, computing and power allocation (JCCPA) algorithm is proposed. More specifically, the original problem is decomposed into two subproblems: 1) power allocation problem and 2) joint caching placement and computing decision problem. The power allocation problem is proved to be a quasi-convex problem, and the closed-form solution is derived. With the optimal transmit power, the joint caching placement and computing decision problem is further divided into two subproblems, which are solved in an iterative manner. Simulation results demonstrate that the proposed algorithms can effectively reduce the weighted sum of delivery delay and energy consumption (outperforms the baselines around 5%–12% within a certain range).