From Instantly Decodable to Random Linear Network Coding

TL;DR

This paper introduces a unified coding framework bridging the gap between RLNC and IDNC, enabling a tradeoff between throughput and decoding delay with practical implementations verified through extensive simulations.

Contribution

It redefines packet generation, unifies RLNC and IDNC under a single framework, and proposes implementations to optimize performance and complexity.

Findings

Performance bounds between RLNC and IDNC established

Coding schemes achieve tunable throughput-delay tradeoff

Simulations confirm improved performance and adaptability

Abstract

Our primary goal in this paper is to traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC) in terms of throughput and decoding delay. We first redefine the concept of packet generation and use it to partition a block of partially-received data packets in a novel way, based on the coding sets in an IDNC solution. By varying the generation size, we obtain a general coding framework which consists of a series of coding schemes, with RLNC and IDNC identified as two extreme cases. We then prove that the throughput and decoding delay performance of all coding schemes in this coding framework are bounded between the performance of RLNC and IDNC and hence throughput-delay tradeoff becomes possible. We also propose implementations of this coding framework to further improve its throughput and decoding delay performance, to manage feedback frequency and coding complexity, or to achieve in-block performance adaption. Extensive simulations are then provided to verify the performance of the proposed coding schemes and their implementations.