Statistical Multiplexing of Homogeneous Streams results in Linear Bandwidth Gains

Abstract

Statistical multiplexing of traffic streams results in reduced network bandwidth requirement. The resulting gain increases with the increase in the number of streams being multiplexed together. However, the exact shape of the gain curve, as more and more streams are multiplexed together, is not known.

In this paper, we first present the generalized result that the statistical gain of combining homogeneous traffic streams, of any traffic type, is a linear function of the number of streams being multiplexed. That is, given a fixed Quality of Service (QoS) constraint, like percentile delay, D, the bandwidth requirement of n streams to satisfy the delay constraint D is n x R x c where R is the bandwidth requirement of a single stream that satisfies the constraint D and c e (0,1]. We present the linear bandwidth gain result, using an extensive simulation study for video traces, specifically, streaming video (IPTV traces) and interactive video (CISCO Telepresence traces).

The linear bandwidth gain result is then verified using analytical tools from two different domains. First, we validate the linearity using Queueing Theory Analysis, specifically using Interrupted Poisson Process (IPP) and Markov Modulated Poisson Process (MMPP) modeling. Second, we formally prove the linear behavior using the Asymptotic Analysis of Algorithms, specifically, the Big-O analysis.

Share and Cite:

B. Anjum, "Statistical Multiplexing of Homogeneous Streams results in Linear Bandwidth Gains," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 1-7. doi: 10.4236/jsea.2012.512B001.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] G. Van der Auwera, "Implications of Smoothing on Sta-tistical Multiplexing of H.264/AVC and SVC Video Streams," IEEE Transactions on Broadcasting, vol. 55, no. 3, pp. 541-558, Sep. 2009.
[2] K. Angrishi, "Anal-ysis of a Real-Time Network using Statistical Network Calculus with Effective Bandwidth and Effective Capacity," in 14th GI/ITG Conference on Measuring, Modeling and Evaluation, 2008, pp. 1-15.
[3] H. Li, C. Huang, and M. Devetsikiotis, “A Robust Adaptive Effec-tive Bandwidth Allocation Technique,” in IEEE Interna-tional Conference on Communications, 2005, pp. 115-119.
[4] K. Ravindran, M. Rabby, and X. Liu, "Bandwidth measurement and management for end-to-end connectivity over IP networks," in First In-ternational Communication Systems and Networks and Workshops, 2009, pp. 1-8.
[5] J. Jeong, S. Jeon, Y. H. Jung, and Y. Choe, "Statistical Multiplexing using scala-ble video coding for layered multicast," in IEEE Interna-tional Symposium on Broadband Multimedia Systems, 2009, pp. 1-5.
[6] H. G. Perros, Connection-Oriented Networks: SONET/SDH, ATM, MPLS, and OPTICAL NETWORKS, Wiley, 2005.
[7] Q. B. Lone, "Bandwidth Allocation for Video Streams Subject To An End-to-End Percentile Delay," MS Thesis, North Carolina State University, 2011.
[8] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 3rd ed. Massachusetts Institute of Technology, 2009.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.