  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.
