Instantly Decodable versus Random Linear Network Coding: A Comparative   Framework for Throughput and Decoding Delay Performance
  This paper studies the tension between throughput and decoding delay performance of two widely-used network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC). A single-hop broadcasting system model is considered that aims to deliver a block of packets to all receivers in the presence of packet erasures. For a fair and analytically tractable comparison between the two coding schemes, the transmission comprises two phases: a systematic transmission phase and a network coded transmission phase which is further divided into rounds. After the systematic transmission phase and given the same packet reception state, three quantitative metrics are proposed and derived in each scheme: 1) the absolute minimum number of transmissions in the first coded transmission round (assuming no erasures), 2) probability distribution of extra coded transmissions in a subsequent round (due to erasures), and 3) average packet decoding delay. This comparative study enables application-aware adaptive selection between IDNC and RLNC after systematic transmission phase.   One contribution of this paper is to provide a deep and systematic understanding of the IDNC scheme, to propose the notion of packet diversity and an optimal IDNC encoding scheme for minimizing metric 1. This is generally NP-hard, but nevertheless required for characterizing and deriving all the three metrics. Analytical and numerical results show that there is no clear winner between RLNC and IDNC if one is concerned with both throughput and decoding delay performance. IDNC is more preferable than RLNC when the number of receivers is smaller than packet block size, and the case reverses when the number of receivers is much greater than the packet block size. In the middle regime, the choice can depend on the application and a specific instance of the problem. 
Parastoo Sadeghi, Mingchao Yu
cs.IT math.IT