Abstract
In an in-home digital network several data streams (audio, video) may run simultaneously over a shared communication device, e.g. a bus. The burstiness of a data stream can be reduced by buffering data at the sending and receiving side, thereby allowing a lower bus share allocation for the stream. In this paper we present an algorithm that determines how much of the bus capacity and buffer space should be allocated to each stream, in order to have a feasible transmission schedule for each stream. Furthermore, the algorithm determines a transmission schedule for each stream, indicating how much data is transmitted over time. We model the problem as a linear program and apply a Dantzig–Wolfe decomposition such that the multiple-stream problem can be solved by repeatedly solving single-stream problems. For these single-stream problems we briefly describe efficient algorithms to solve them.
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REFERENCES
Dantzig, G. B. and P. Wolfe, “Decomposition principle for linear programs,” Operations Res, 8, 101–111 (1960).
Dantzig, G. B. and P. Wolfe, “The decomposition algorithm for linear programming,” Econometrica, 29, 767–778 (1961).
Den Boef, E., W. F. J. Verhaegh, and J. Korst, “Smoothing streams in an in-home digital network: optimization of bus and buffer usage,” Accepted for publication in Telecommunication Systems, special issue on Multimedia Home Telecommunication Systems.
Feng, W.-C., “Rate-constrained bandwidth smoothing for the delivery of stored video,” in Proceedings SPIE Multimedia Networking and Computing, (1997) pp. 316–327.
Feng, W.-C., and J. Rexford, “Performance evaluation of smoothing algorithms for transmitting prerecorded variable-bit-rate video,” IEEE Trans. on Multimedia, 1, 302–313 (1999).
Gill, P. E., W. Murray, and M. H. Wright, Practical Optimization, Academic Press, San Diego, California, 7th edn., 1988.
Ho, J. K. and E. Loute, “An advanced implementation of the Dantzig-Wolfe decomposition algorithm for linear programming,” Math Program., 20, 303–326 (1981).
Kang, S. and H. Y. Yeom, “Aggregated smoothing of VBR video streams,” in Proceedings 14th International Conference on Information Networking (ICOIN-14), (2000).
McManus, J. M. and K. W. Ross, “A dynamic programming methodology for managing prerecorded VBR sources in packet-switched networks,” Telecommun. Syst., 9, 223–247 (1998).
Montvay, A., “System management and automatic reconfiguration algorithms for in-home digital networks,” in Proceedings ACM Multimedia `99 (Part 2), (1999) pp. 213–214.
Papadimitriou, C. H. and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, Prentice Hall, Englewood Cliffs, (1982).
Rexford, J. and Don Towsley, “Smoothing variable-bit-rate video in an internetwork,” IEEE/ACM Trans. Network., 7, 202–215 (1999).
Salehi, J. D., Z.-L. Zhang, J. Kurose, and D. Towsley, “Supporting stored video: Reducing rate variability and end-to-end resource requirements through optimal smoothing,” IEEE/ACM Trans. Network., 6, 397–410 (1998).
Sanjay, G. and S. V. Raghavan, “Fast techniques for the optimal smoothing of stored video,” Multimedia Syst., 7, 222–233 (1999).
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Den Boef, E., Verhaegh, W.F. & Korst, J. Bus and Buffer Usage in In-Home Digital Networks: Applying the Dantzig–Wolfe Decomposition. Journal of Scheduling 7, 119–131 (2004). https://doi.org/10.1023/B:JOSH.0000014068.89991.c4
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DOI: https://doi.org/10.1023/B:JOSH.0000014068.89991.c4