Enabling a Tradeoff between Completion Time and Decoding Delay in Instantly Decodable Network Coded Systems

Abstract

This paper studies the complicated interplay of the completion time (as a measure of throughput) and the decoding delay performance in instantly decodable network coded (IDNC) systems over wireless broadcast erasure channels with memory, and proposes two new algorithms that improve the balance between the completion time and decoding delay of broadcasting a block of packets. We first formulate the IDNC packet selection problem that provides joint control of the completion time and decoding delay as a statistical shortest path (SSP) problem. However, since finding the optimal packet selection policy using the SSP technique is computationally complex, we employ its geometric structure to find some guidelines and use them to propose two heuristic packet selection algorithms that can efficiently improve the balance between the completion time and decoding delay for broadcast erasure channels with a wide range of memory conditions. It is shown that each one of the two proposed algorithms is superior for a specific range of memory conditions. Furthermore, we show that the proposed algorithms achieve an improved fairness in terms of the decoding delay across all receivers.

Distributed relay selection with network coding for data dissemination in vehicular ad hoc networks

“…By doing so, IDNC guarantees a subset of (or if possible, all) receivers be able to instantly decode one of their wanted packets upon successful reception of the coded packet. [27][28][29] Moreover, IDNC encoding can be implemented using binary XOR, which eliminates complicated operations over large Galois fields as required by RLNC.…”

Instantly decodable network coding for real-time scalable video broadcast over wireless networks

“…However, the works in [34,38] showed that finding the optimal IDNC schedule for wireless broadcast of a set of packets is computationally intractable due to the curse of dimensionality of the dynamic programming approach. Therefore, to efficiently solve the optimization problem in (4) with much lower computational complexity, we draw several guidelines for the prioritized IDNC algorithms in the following three subsections.…”

“…This heuristic algorithm selects maximal cliques κ c and κ b based on a greedy vertex search over IDNC graphs G 1: c and G 1: b (κ c ), respectively. A similar greedy vertex search approach was studied in [34,38] due to its computational simplicity. However, the works in [34,38] solved different problems and ignored the dependency between source packets and the hard deadline.…”

“…In [34,38], the authors addressed the problem of minimizing the number of transmissions required for broadcasting a set of packets in IDNC systems and formulated the problem into a stochastic shortest path (SSP) framework. However, the works in [31,32,34,[36][37][38] neither considered dependency between source packets as is present in the scalable video applications nor considered explicit packet delivery deadline. Several other works in IDNC considered different importance of packets and prioritized packets differently in coding decisions.…”

Performance characterization and transmission schemes for instantly decodable network coding in wireless broadcast

“…Although G-IDNC has been extensively studied under various wireless broadcast settings, including basic ones [12,12,[22][23][24][25][26][27][28] and those with limited/lossy feedback [29,30] or with hard deadline [31], most developed algorithms are heuristics, leaving the optimal G-IDNC in terms of throughput and APDD still unknown or intractable due to prohibitively large computational complexity. Hence in this paper, we take a step back, aiming to understand the performance limits and optimal implementations of a sub-class of G-IDNC, namely, S-IDNC.…”