A Graph Model for Opportunistic Network Coding

TL;DR

This paper introduces a graph-based model for a subclass of opportunistic network coding, enabling optimized decoding and performance improvements close to optimal solutions.

Contribution

It extends graph-based IDNC analysis to a specific ONC subclass by defining generalized vertices and aggregation, facilitating performance optimization.

Findings

Improves completion time over IDNC

Close to optimal performance achieved

Graph model effectively represents ONC scenarios

Abstract

Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.