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.
Highlights
As forecasted by Cisco, mobile video will account for 79% of total mobile data traffic by 2022 [1]
The contributions of our work can be summarized in the following: (i) We propose a broadcast mechanism where multisources cooperatively provide service combining content caching and content delivery policy (ii) We design an optimization model to maximize the volume of received data which is formulated as an integer nonlinear programming (INLP) problem and improve the quality of experience (QoE) of desired content by dynamically determining buffer action (iii) We apply the McCormick envelope method and Lagrange relaxation to convert the initial INLP problem into several decoupled subproblems which can be solved through a distributed algorithm (iv) Simulation results show that our cooperative broadcast schemes, distributed algorithm, and buffer action determination algorithm can promote QoE of desired content
We propose a cooperative broadcast optimization to improve the QoE for vehicular video streaming
Summary
As forecasted by Cisco, mobile video will account for 79% of total mobile data traffic by 2022 [1]. We propose a cooperative broadcast mechanism where multisources provide desired content to vehicle requester simultaneously. (i) We propose a broadcast mechanism where multisources cooperatively provide service combining content caching and content delivery policy (ii) We design an optimization model to maximize the volume of received data which is formulated as an INLP problem and improve the QoE of desired content by dynamically determining buffer action (iii) We apply the McCormick envelope method and Lagrange relaxation to convert the initial INLP problem into several decoupled subproblems which can be solved through a distributed algorithm (iv) Simulation results show that our cooperative broadcast schemes, distributed algorithm, and buffer action determination algorithm can promote QoE of desired content.
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