Decoding-Delay-Controlled Completion Time Reduction in Instantly Decodable Network Coding

Abstract

For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution in persistent erasure channels. We first derive the decoding-delay-dependent expressions of the users' and overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we design two novel heuristics that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. We, then, extend our study to the limited feedback scenario. Simulation results show that our new algorithms achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.

Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

“…Simulations showed that this approach largely outperforms the classical approach using the SSP formulation. The same group of authors extended the study in [152] to the case of lossy intermittent feedback over PECs. Whereas previous works using PECs considered zero error probability (good state always correct and bad state always in error), reference [152] used a non-zero error GEC.…”

“…The same group of authors extended the study in [152] to the case of lossy intermittent feedback over PECs. Whereas previous works using PECs considered zero error probability (good state always correct and bad state always in error), reference [152] used a non-zero error GEC. In contrast with zero error probability in which if an erasure occurs (or do not occur), the sender can surely determine that the channel is in bad state (or good state), in the non zero error GEC this information is never sure and should always be estimated.…”

“…In contrast with zero error probability in which if an erasure occurs (or do not occur), the sender can surely determine that the channel is in bad state (or good state), in the non zero error GEC this information is never sure and should always be estimated. The algorithm proposed in [152] is shown to outperform the classical blind approach to solve the imperfect feedback in completion time minimization. Moreover, the authors propose a low complexity algorithm with comparable performances based on binary particle swarm optimization.…”

“…This formulation using binary PSO in [152] allowed lower complexity since it does not require the construction of the multi layer G-IDNC graph and its update at each selected packet. Moreover, for a precise number of particular and a specific initialization, the authors proven that the overall system will converge which eliminate the inconvenient of PSO and keeps only its advantages.…”

A Game-Theoretic Framework for Network Coding Based Device-to-Device Communications

“…The expressions of these packet combinations can be found in [37] for the completion time and the maximum decoding delay reduction, and in [25] for the sum decoding delay minimization.…”