User Allocation Strategy for Interactive Live Streaming in Edge Computing

Deploying interactive live streaming in edge computing environment enable us to offload the transcoding and delivery cost to network edge and provide service with lower latency via the edge servers near the users. However, there is a problem of user allocation in the real complex multi-server and multi-user scenarios, leading to a poor quality of experience (QoE). In order to improve QoE of overlapping coverage areas, it is necessary to select edge servers for individual live streaming users according to their needs and to allocate server resources reasonably. First, the analysis of real-world data sets reveals that most users are in the overlapping coverage area of multiple base stations whose interaction needs vary. Then, a QoE model suitable for edge computing scenarios is proposed according to the analysis, which is based on the characteristics of interactive live streaming and comprehensively considers the interactive and video viewing experience of users. Finally, an efficient live streaming user allocation algorithm is designed to optimize the user QoE in the overlapping coverage area of multiple edge servers. Simulation experiments show that this strategy can provide users with high bit rate and low latency streaming while controlling the edge servers switching and bit rate jitter, thus improving the QoE of users by more than 19% compared with the other strategies.