NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

TL;DR

This paper introduces a distributed rate allocation algorithm for streaming applications using NUM with S-curve utilities, employing sequential convex programming to handle non-convexities and ensure convergence to a local optimum.

Contribution

It develops a novel SCP-based method to solve non-convex NUM problems with S-curve utilities, enabling distributed implementation and convergence guarantees.

Findings

Algorithm converges to a local optimum under mild conditions.

Numerical results show effective and tractable convergence.

Proposed method outperforms existing approaches in handling non-convex utilities.

Abstract

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with such a transformed NUM, we leverage Sequential Convex Programming (SCP) approach to approximate the non-convex problem by a series of convex ones. Based on this approach, we propose a distributed rate allocation algorithm and demonstrate that under mild conditions, it converges to a locally optimal solution of the original NUM. Numerical results validate the effectiveness, in terms of tractable convergence of the proposed rate allocation algorithm.