Video caching and scheduling with edge cooperation

In this paper, we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming. The aim is to reduce the average initial buffer delay and improve the quality of user experience. Considering the difference between global and local video popularities and the time-varying characteristics of video popularity, a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay. Based on both long-term content popularity and short-term content popularity, the proposed caching solution is decouple into the proactive cache stage and the cache update stage. In the proactive cache stage, we develop a proactive cache placement algorithm that can be executed in an off-peak period. In the cache update stage, we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay. Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.