Joint Caching and User Association Optimization for Adaptive Bitrate Video Streaming in UAV-Assisted Cellular Networks

Edge caching and adaptive bitrate video streaming are two promising techniques to ensure a good video viewing experience. Edge caching can bring contents closer to users to alleviate redundant content transmissions, reduce user-perceived delay and improve transmission capability. Adaptive bitrate video streaming is able to adaptively adjust video quality based on time-varying network conditions and different users’ preference. Due to the strong coupled relationship between caching and user association, in this article, we focus on the issue of joint caching and user association optimization for adaptive bitrate video streaming in UAV-assisted cellular networks. First, we formulate the optimization problem as a non-linear integer programming (NLIP) to minimize the content delivery delay. To solve this challenging NP-hard problem, a heuristic algorithm based on quantum-inspired evolutionary algorithm (QEA) is proposed to obtain the best caching and user association solutions iteratively. Finally, simulations are conducted to demonstrate that compared with three benchmark algorithms without joint optimization of caching and user association, the proposed algorithm can greatly improve users’ video viewing experience and achieve better system performance in terms of reducing the total content delivery delay.