Interest-based QoE-Driven Video Caching and Adaptation in Edge Network
Abstract
With the emerging demand of video requests in mobile networks, Multi-access Edge Computing (MEC) techniques were implemented in many of the 5G network architecture designs. Also, as social networking serves as a huge role in most of the mobile applications, utilizing the social influence factors into caching mechanism would provide better networking services. In this paper, we proposed a video content caching framework intended to provide non-delay playback services for future requests of the same video content. In this way, the users in the same Radio Access Network (RAN) that shares the same base station having a MEC Server and cache storages should benefit from the caching mechanism to significantly reduce transmission overhead and optimize caching resources while maintaining the best quality of experience (QoE) for video viewing as possible. Finally, the experiments show the proposed caching mechanism method offers much better performance than traditional random, most popular and least frequent used (LFU) caching algorithms on the average hit ratio and QoE.