Collaborative Caching and Scheduling for Live Streaming in Mobile Edge Computing

Abstract

With the vigorous increase in live video traffic today, more and more live users require low-latency high-quality video streaming. To this end, there have been many Adaptive Bitrate Streaming (ABR) algorithms to adapt video bitrate to network conditions, and most of them are implemented in the client side. Such algorithms typically can only optimize the quality of experience (QoE) for a single user, but are agnostic to the comprehensive video streaming performance of multiple users. The client-based ABR algorithms also cannot provide sufficient utilization of network resources due to the lack of multi-user perspective. Mobile edge computing (MEC) can achieve lower response latency and can obtain network states of multiple users at edge servers, which is a most applicable technology for mobile live streaming. In this paper, we propose a collaborative caching and scheduling (CCS) mechanism for live streaming services in the MEC environment, aiming to improve the overall viewing QoE for multiple users. CCS provides integrated segment scheduling and allocation of bandwidth and cache resources in the edge network to improve the utilization of resources. At the same time, CCS further explores the larger optimization space provided by scalable video coding (SVC) for enhancing the quality of caching and scheduling solutions. According to our simulation results, CCS can provide a better comprehensive QoE for users compared with counterparts.