Layered multicast rate control based on Lagrangian relaxation and dynamic programming

In this paper, we address the rate control problem for layered multicast traffic, with the objective of solving a generalized throughput/fairness objective. Our approach is based on a combination of Lagrangian relaxation and dynamic programming. Unlike previously proposed dual-based approaches, the algorithm presented in this paper scales well as the number of multicast groups in the network increases. Moreover, unlike all existing approaches, our approach takes into account the discreteness of the receiver rates that is inherent to layered multicasting. We show analytically that our algorithm converges and yields rates that are approximately optimal. Simulations carried out in an asynchronous network environment demonstrate that our algorithm exhibits good convergence speed and minimal rate fluctuations

[1]  Supratim Deb,et al.  Congestion control for fair resource allocation in networks with multicast flows , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[2]  Leandros Tassiulas,et al.  A scalable low-overhead rate control algorithm for multirate multicast sessions , 2002, IEEE J. Sel. Areas Commun..

[3]  Leandros Tassiulas,et al.  Optimization based rate control for multirate multicast sessions , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[4]  Frank Kelly,et al.  Charging and rate control for elastic traffic , 1997, Eur. Trans. Telecommun..

[5]  Donald F. Towsley,et al.  Decentralized Computation of Weighted Max-Min Fair Bandwidth Allocation in Networks with Multicast Flows , 2001, IWDC.

[6]  Naum Zuselevich Shor,et al.  Minimization Methods for Non-Differentiable Functions , 1985, Springer Series in Computational Mathematics.

[7]  Leandros Tassiulas,et al.  Fair allocation of utilities in multirate multicast networks: a framework for unifying diverse fairness objectives , 2002, IEEE Trans. Autom. Control..

[8]  Steven McCanne,et al.  Receiver-driven layered multicast , 2001 .

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

[10]  Leandros Tassiulas,et al.  Fair allocation of discrete bandwidth layers in multicast networks , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[11]  Leandros Tassiulas,et al.  Distributed algorithms for computation of fair rates in multirate multicast trees , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[12]  R. K. Shyamasundar,et al.  Introduction to algorithms , 1996 .

[13]  G. Nemhauser,et al.  Integer Programming , 2020 .

[14]  Sanjoy Paul.  Layered Video Multicast with Retransmission (LVMR) , 1998 .

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

[16]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

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

[18]  Don Towsley,et al.  The impact of multicast layering on network fairness , 2002, TNET.

[19]  Steven McCanne,et al.  Low-Complexity Video Coding for Receiver-Driven Layered Multicast , 1997, IEEE J. Sel. Areas Commun..