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

TL;DR

This paper introduces a novel IDNC graph and algorithms to optimize real-time video distribution in device-to-device wireless networks, reducing video distortion before deadlines.

Contribution

The paper proposes a new IDNC graph and a two-stage algorithm to efficiently minimize video distortion in real-time D2D communications.

Findings

Improved video quality with proposed algorithms.

Effective reduction of video distortion before deadlines.

Enhanced performance over existing IDNC methods.

Abstract

In this paper, we study the problem of distributing a real-time video sequence to a group of partially connected cooperative wireless 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 transmission 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 conflict-free decisions in one unified framework. Moreover, a real-time 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 process (MDP) problem. However, the backward induction algorithm that finds the optimal policy of the MDP formulation has high modelling and computational complexities. To reduce these complexities, we further design a two-stage maximal independent set selection algorithm, which can efficiently reduce the mean video distortion before the deadline. Simulation results over a real video sequence show that our proposed IDNC algorithms improve the received video quality compared to the existing IDNC algorithms.