A New QoE-Driven Video Cache Allocation Scheme for Mobile Cloud Server

Abstract

With the advent of mobile cloud computing, video cache technologies at local cellular networks have attracted extensive attention. Nevertheless, existing video cache allocation schemes mostly made decisions only according to the video coding requirements, without considering users’ individual perception for the video service. In this paper, we propose a new video cache allocation scheme with the consideration of quality of experience (QoE) of users under limited storage space. We make use of the linear regression algorithm to map the relationship between the requested video rate, the replied video rate, the channel condition and the QoE value, which then helps to obtain the different video rates to be stored in the server. Meanwhile, we define the parameter to represent the popularity of a video clip. We optimize the cache space allocation for each video clip based on these parameters in the mobile cloud server of local cellular networks. The experiments demonstrate that the proposed scheme has a better performance in terms of the overall QoE of users with the constraint of the total cache size.