CVC: A Collaborative Video Caching Framework Based on Federated Learning at the Edge

Abstract

With the rapid development of Internet social media platforms and the popularization of smart terminal devices, video services such as short videos have surged. However, the massive amount of smart devices connected to the core network causes the increase of load on the backhaul link, which makes traditional cloud computing unable to meet the low-latency requirement of user for video services fully. To this end, we propose to implement proactive video caching at the edge to improve the quality of user experience. Thus, a novel collaborative video caching framework at the edge, CVC, is proposed. In this framework, we present a video request prediction model based on federated learning, aiming to reduce communication costs. Also, we design two methods to improve the hit rate and reduce latency, including a collaborative caching decision method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CCD</i> ) and a collaborative service response method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSR</i> ). Under the video service scenario, the experimental results show that CVC can improve the cache hit rate and reduce the waiting delay of users compared with the traditional caching algorithms. Simultaneously, CVC can also reduce the overall system’s communication cost and caching cost.