Joint Coding and Scheduling Optimization in Wireless Systems with Varying Delay Sensitivities

TL;DR

This paper develops a unified framework for optimizing coding and scheduling in wireless broadcast systems with diverse delay sensitivities, balancing throughput and delay constraints through advanced mathematical programming techniques.

Contribution

It introduces a novel delay-sensitive optimization framework using GGP and Signomial Programming, addressing joint coding and scheduling for multiple receivers with varying delay needs.

Findings

Trade-off characterization between delay and throughput.

Adaptive optimization of coding and scheduling parameters.

Approximation methods for NP-hard optimization problems.

Abstract

Throughput and per-packet delay can present strong trade-offs that are important in the cases of delay sensitive applications.We investigate such trade-offs using a random linear network coding scheme for one or more receivers in single hop wireless packet erasure broadcast channels. We capture the delay sensitivities across different types of network applications using a class of delay metrics based on the norms of packet arrival times. With these delay metrics, we establish a unified framework to characterize the rate and delay requirements of applications and optimize system parameters. In the single receiver case, we demonstrate the trade-off between average packet delay, which we view as the inverse of throughput, and maximum ordered inter-arrival delay for various system parameters. For a single broadcast channel with multiple receivers having different delay constraints and feedback delays, we jointly optimize the coding parameters and time-division scheduling parameters at the transmitters. We formulate the optimization problem as a Generalized Geometric Program (GGP). This approach allows the transmitters to adjust adaptively the coding and scheduling parameters for efficient allocation of network resources under varying delay constraints. In the case where the receivers are served by multiple non-interfering wireless broadcast channels, the same optimization problem is formulated as a Signomial Program, which is NP-hard in general. We provide approximation methods using successive formulation of geometric programs and show the convergence of approximations.