A Suboptimal Network Utility Maximization Approach for Scalable Multimedia Applications

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.

[1]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[2]  Aggelos K. Katsaggelos,et al.  Rate-distortion optimal video summary generation , 2005, IEEE Transactions on Image Processing.

[3]  A. Robert Calderbank,et al.  Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures , 2007, Proceedings of the IEEE.

[4]  Ness B. Shroff,et al.  Non-convex optimization and rate control for multi-class services in the Internet , 2005, IEEE/ACM Transactions on Networking.

[5]  Jianwei Huang,et al.  Joint Source Adaptation and Resource Pricing for Multi-User Wireless Video Streaming , 2007 .

[6]  Ming-Ting Sun,et al.  Digital Video Transcoding , 2005, Proceedings of the IEEE.

[7]  Jens-Rainer Ohm,et al.  Advances in Scalable Video Coding , 2005, Proceedings of the IEEE.

[8]  S. Shenker Fundamental Design Issues for the Future Internet , 1995 .

[9]  Ellen W. Zegura,et al.  Utility max-min: an application-oriented bandwidth allocation scheme , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[10]  Steven H. Low,et al.  Optimization flow control—I: basic algorithm and convergence , 1999, TNET.

[11]  Frank Kelly,et al.  Rate control for communication networks: shadow prices, proportional fairness and stability , 1998, J. Oper. Res. Soc..

[12]  Yu Sun,et al.  Video transcoding: an overview of various techniques and research issues , 2005, IEEE Transactions on Multimedia.

[13]  Shengyu Zhang,et al.  Distributed rate allocation for inelastic flows , 2005, IEEE/ACM Trans. Netw..

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  A. Robert Calderbank,et al.  Content-Aware Distortion-Fair Video Streaming in Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[16]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[17]  Jun Xin,et al.  Video Adaptation : Concepts , Technologies , and Open Issues , .

[18]  Aggelos K. Katsaggelos,et al.  Joint Source Adaptation and Resource Allocation for Multi-User Wireless Video Streaming , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Mung Chiang,et al.  Nonconcave network utility maximization through sum of squares method , 2005, IEEE Conference on Decision and Control.

[20]  Heiko Schwarz,et al.  Overview of the Scalable Video Coding Extension of the H.264/AVC Standard , 2007, IEEE Transactions on Circuits and Systems for Video Technology.