  This paper addresses rate control for transmission of scalable video streams
via Network Utility Maximization (NUM) formulation. Due to stringent QoS
requirements of video streams and specific characterization of utility
experienced by end-users, one has to solve nonconvex and even nonsmooth NUM
formulation for such streams, where dual methods often prove incompetent.
Convexification plays an important role in this work as it permits the use of
existing dual methods to solve an approximate to the NUM problem iteratively
and distributively. Hence, to tackle the nonsmoothness and nonconvexity, we aim
at reformulating the NUM problem through approximation and transformation of
the ideal discretely adaptive utility function for scalable video streams. The
reformulated problem is shown to be a D.C. (Difference of Convex) problem. We
leveraged Sequential Convex Programming (SCP) approach to replace the nonconvex
D.C. problem by a sequence of convex problems that aim to approximate the
original D.C. problem. We then solve each convex problem produced by SCP
approach using existing dual methods. This procedure is the essence of two
distributed iterative rate control algorithms proposed in this paper, for which
one can show the convergence to a locally optimal point of the nonconvex D.C.
problem and equivalently to a locally optimal point of an approximate to the
original nonconvex problem. Our experimental results show that the proposed
rate control algorithms converge with tractable convergence behavior.
