Network Coding Aided Collaborative Real-Time Scalable Video Transmission in D2D Communications

There has been an increasing demand for providing real-time video streaming services in the next-generation cellular networks. To improve the quality of such services without additional infrastructure, neighboring devices can recover missing packets by using network coding aided collaborative transmission via device-to-device (D2D) communication. However, most existing work in this area had not considered the issue of how to schedule such coding aided collaborative transmissions effectively for supporting real-time scalable video applications in such environment. In this paper, we study how to improve the quality of real-time scalable video services by efficiently scheduling coding aided collaborative transmissions. We first formulate the problem of optimal collaborative transmission scheduling that determines the optimal transmitting sequence and coding pattern at each transmitting device, which is shown to be NP-hard. To address this problem, we propose a new weight function for measuring the quality of a coding pattern by considering packet recovery gain and potential video decoding gain at receivers. Based on this new weight function, we propose a low complexity centralized algorithm using global state information and an efficient distributed mechanism supporting localized operations in dynamic environment. We deduce their computational complexities. Simulation results verify that the proposed solution outperforms the representative work in the literature.