Adaptive Bit Rate capable video caching and scheduling

Adaptive Bit Rate Streaming (ABR) has become a popular video delivery technique, credited to improving the quality of delivered video on wireless networks. At the same time, recent research has shown video caching in the Radio Access Network (RAN) can be a promising way to increase capacity of the network while reducing video latency and improving video quality of experience. In this work, we investigate the opportunities and challenges of combining the advantages of ABR streaming and RAN caching to maximize the video capacity of wireless networks. Since with ABR, each video is divided into multiple segments, chunks, and each chunk can be requested at different bit rates, the caching requirements are very different and challenging; a cache hit will require not only the presence of a specific video chunk, but also the availability of the desired bit rate. One way to solve this problem is to cache all variants of a video, but this approach may significantly increase storage and backhaul requirements or reduce the number of unique videos that can be cached. In this paper, we introduce a framework consisting of rate adaptation algorithm, video caching and processing within the wireless cloud, with the aim to improve video capacity of the wireless network and satisfy or exceed QoE of each video request. To achieve this goal, we propose a new ABR algorithm, along with an ABR aware Least Recently Used (LRU) caching policy with Processing (ABRLRU-P) to support caching of video chunks with different bit rates. Using our MATLAB statistical simulation framework, we demonstrate a capacity improvement of up to 83% when ABR is used with RAN caching and processing using the ABR-LRU-P policy compared with using no RAN caching and video processing. Further, using ABR along with ABRLRU-P caching policy can improve the capacity by 68% compared with using ABR and a straightforward static LRU caching policy which fetches all video bit rate versions of a video chunk upon a cache miss.