Conference Paper

A Suboptimal Network Utility Maximization Approach for Scalable Multimedia Applications.

Sch. of Comput. Sci., IPM, Tehran, Iran
DOI: 10.1109/GLOCOM.2009.5425607 Conference: Proceedings of the Global Communications Conference, 2009. GLOBECOM 2009, Honolulu, Hawaii, USA, 30 November - 4 December 2009
Source: IEEE Xplore

ABSTRACT Wired and wireless data networks have witnessed an explosive growth of inelastic traffics such as real-time or media streaming applications. Recently, applications relying on layered encoding schemes appeared in the context of live-streaming and video and audio delivery applications. This paper addresses the Network Utility Maximization (NUM) for scalable multimedia transmission which is relying on layered encoding schemes. Nonconvexity of the NUM problem for such applications makes dual-based approaches incompetent, whereby achieving optimality proves quite challenging. We adopt the staircase utility function and formulate the underlying optimization problem. To tackle the non-convexity of the problem, we use a smooth approximation of the staircase utility function and propose a dual-based distributed algorithm for rate allocation and bandwidth sharing in such scenarios. Numerical results show that the proposed algorithm achieves suboptimal yet efficient solution.

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    ABSTRACT: Receiver heterogeneity of a P2P network can be effectively addressed by scalable video streams. Due to the discontinuous nature of scalable video, traditional convex-optimization approach is not applicable. We propose a message-passing based approach for optimization using the sum- product update algorithm. Advantage of this simple but elegant approach over other heuristic-based algorithm is that the optimization algorithm itself is independent of the underlying constraints. The algorithm iteratively updates layer allocation decision based on a given set of codewords. The codewords are binary representation of various network and video constraints. Therefore, any number of constraints can be used to generate a set of codewords without modifying the algorithm. To the best of our knowledge, this is the first work that systematically addresses the scalable video optimization problem. Preliminary simulation with up to 8 layers shows that the sum- product update process achieves an average layer delivery of 95% or higher.
    Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on; 09/2011
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    ABSTRACT: 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.
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