Partially Blind Instantly Decodable Network Codes for Lossy Feedback Environment

TL;DR

This paper develops and evaluates partially blind network coding strategies for multicast scenarios with lossy feedback, aiming to minimize completion and decoding delays despite uncertainties in packet reception feedback.

Contribution

It extends existing IDNC models to lossy feedback environments, introducing new algorithms for delay minimization under feedback uncertainty.

Findings

Proposed algorithms outperform existing methods in delay reduction.

Achieves robust performance even with high feedback loss rates.

Provides a maximum likelihood approach for network state estimation.

Abstract

In this paper, we study the multicast completion and decoding delay minimization problems of instantly decodable network coding (IDNC) in the case of lossy feedback. In such environments, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient partially blind policies that handle the completion and decoding delays of IDNC in such environment, we first extend the perfect feedback formulation in [2], [3] to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback, and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in [3]. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to derive the optimal policy to reduce the decoding delay in such lossy feedback environment. Results show that our proposed solution both outperforms other approaches and achieves a tolerable degradation even at relatively high feedback loss rates.