QoE-Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

Abstract

The popularity of online vehicular video has caused enormous information requests in Internet of vehicles (IoV), which brings huge challenges to cellular networks. To alleviate the pressure of base station (BS), Roadside Units (RSUs) and vehicle peers are introduced to collaboratively provide broadcast services to vehicle requesters where vehicles act as both service providers and service requesters. In this paper, we propose an efficient framework leveraging scalable video coding (SVC) technique to improve quality of experience (QoE) from two perspectives: (1) maximizing the data volume received by all requesters and (2) determining buffer action based on playback fluency and average playback quality. For (1), potential providers cooperate to determine the precached video content and delivery policy with the consideration of vehicular mobility and wireless channel status. If one provider fails, other sources will complement to provide requested content delivery. Therefore, their cooperation can improve the QoE and enhance the service reliability. For (2), according to buffer occupancy status, vehicle requesters manage buffer action whether to buffer new segments or upgrade the enhancement level of unplayed segment. Furthermore, the optimization of the data volume is formulated as an integer nonlinear programming (INLP) problem, which can be converted into some linear integer programming subproblems through McCormick envelope method and Lagrange relaxation. Numerical simulation results show that our algorithm is effective in improving total data throughput and QoE.