Network Coding for Video Distortion Reduction in Device-to-Device Communications

Abstract

In this paper, we study the problem of distributing a real-time video sequence to a group of partially connected cooperative \nwireless devices using instantly decodable network coding (IDNC). In such a scenario, the coding conflicts occur to service multiple devices with an immediately decodable packet, and the \ntransmission conflicts occur from simultaneous transmissions of multiple devices. To avoid these conflicts, we introduce a novel IDNC graph that represents all feasible coding and transmission \nconflict-free decisions in one unified framework. Moreover, a realtime video sequence has a hard deadline and unequal importance of video packets. Using these video characteristics and the new IDNC graph, we formulate the problem of minimizing the mean video distortion before the deadline as a finite horizon Markov decision \nprocess (MDP) problem. However, the backward induction algorithm that finds the optimal policy of the MDP formulation has high modeling and computational complexities. To reduce these \ncomplexities, we further design a two-stage maximal independent set selection algorithm, which can efficiently reduce the mean video \ndistortion before the deadline. Simulation results over a real video sequence show that our proposed IDNC algorithms improve the received \nvideo quality compared with the existing IDNC algorithms.