4 research outputs found
NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming
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.Comment: 6 pages, conference submissio