Packet Completion Time Minimization via Joint D2D and Cellular Communication: A Unified Network Coding Approach

TL;DR

This paper proposes a unified network coding approach combining cellular and D2D links to minimize packet completion time for real-time content delivery, using graph theory and heuristics for efficiency.

Contribution

It introduces a novel two-layer IDNC conflict graph model and an optimal NP-hard solution, along with a low-complexity heuristic for practical implementation.

Findings

Significant reduction in packet completion time compared to existing methods

Effective use of graph theory to model and solve the coding problem

Heuristic approach achieves near-optimal performance with lower complexity

Abstract

This paper tackles the problem of transmitting a common content to a number of cellular users by means of instantly decodable network coding (IDNC) with the help of intermittently connected D2D links. Of particular interest are broadcasting real-time applications such as video-on-demand, where common contents may be partially received by cellular users due to packet erasures over cellular links. Specifically, we investigate the problem of packet completion time, defined as the number of transmission slots necessary to deliver a common content to all users. Drawing on graph theory, we develop an optimal packet completion time strategy by constructing a two-layer IDNC conflict graph. The higher-layer graph permits us to determine all feasible packet combinations that can be transmitted over the cellular link, while the lower-layer graph enables us to find all feasible network coded packets and identify the set of users that can generate and transmit these packets via intermittently connected D2D links. By combining the higher-layer and the lower-layer IDNC conflict graphs, we demonstrate that finding the optimal IDNC packets to minimize the packet completion time problem is equivalent to finding the maximum independent set of the two-layer IDNC conflict graph, which is known to be an NP-hard problem. We design a scheme that invokes the Bron-Kerbosch algorithm to find the optimal policy. To circumvent the high computational complexity required to reach the global optimum, we establish a polynomial-time solvable low-complexity heuristic to find an efficient sub-optimal solution. The effectiveness of our proposed scheme is verified through extensive numerical results which indicate substantial performance improvement in comparison with existing methods.