ABSTRACT
We consider the problem of smoothing real-time streams (such as video streams), where the goal is to reproduce a variable-bandwidth stream remotely, while minimizing bandwidth cost, space overhead, and playback delay. We focus on lossy schedules, where some bytes may be dropped due to limited bandwidth or space. We present the following results. First, we determine the optimal tradeoff between buffer space, queuing delay, and link bandwidth for lossy smoothing schedules. Specifically, this means that if one of these parameters is under our control, we can precisely calculate the optimal value which minimizes data loss while avoiding resource wastage. The tradeoff is accomplished by a simple generic algorithm, that allows one some freedom in choosing which data to discard. This algorithm is very easy to implement both at the server and at the client, and it enjoys the nice property that only the server decides which data to discard, and the client needs only to reconstruct the stream.
In a second set of results we study the case where different parts of the data have different importance, modeled by assigning a real “weight” to each byte in the stream. For this setting we use competitive analysis, i.e., we compare the weight delivered by on-line algorithms to the weight of an optimal off-line schedule using the same resources. We prove that a natural greedy algorithm is 4-competitive. We also prove a lower bound of 1.25 on the competitive ratio of any deterministic on-line algorithm. Finally, we give a few experimental results which show that smoothing is extremely effective in practice, and that the greedy algorithm performs very well in the weighted case.
- 1.www.nmis.org/Newslnteractive/CNN/Newsroom,Google Scholar
- 2.MPEG-I standard (ISO/IEC 11172), 1992.Google Scholar
- 3.MPEG-2 standard (ISO/IEC DIS 13818), 1994.Google Scholar
- 4.A. Borodin and R. El-Yaniv. Online Computation and CompetitiveAnalysis. Cambridge University Press, 1998. Google ScholarDigital Library
- 5.R.-I. Chang, M.-C. Chen, J.-M. Ho, and M.-T. Ko. An effective and efficient traffic smoothing scheme for delivery of online VBR media streams. In Proceedings oflEEE INFOCOM, 1999.Google Scholar
- 6.N. G. Duffield, K. K. Ramakrishnan, and A. R. Reibman. SAVE: An algorithm for smoothed adaptive video over explicit rate networks. IEEE/ACM Transactions on Networking, 6(6):717-728, 1998. Google ScholarDigital Library
- 7.W. Feng and J. Rexford. Performance evaluation of smoothing algorithms for transmitting prerecorded variable-bit-rate video. IEEE Trans. on Multimedia, Sept. 1999. To appear. Google ScholarDigital Library
- 8.M. Grosslauser, S. Keshav, and D. N. C. Tse. RCBR: A simple and efficient service for multiple time-scale traffic. IEEE/ACM Transactions on Networking, 5(6):741- 755, Dec. 1997. Google ScholarDigital Library
- 9.T. Y. J. Ni and D. Tsang. A CBR transport technique for MPEG-2 video-on-demand connections over ATM networks. In Proc. IEEE ICC 96, pages 1391-1395, June 1996.Google Scholar
- 10.Z. jiang and L. Kleinrock. A general optimal smoothing video algorithm. In Proc. IEEE INFOCOM, Mar. 1999.Google Scholar
- 11.S. Keshav. An Engineering Approach to Computer Networking. Addison-Wesley Publishing Co., 1997. Google ScholarDigital Library
- 12.S. S. Lain, S. Chow, and D. K. Y. Yau. An algorithm for lossless smoothing of MPEG video. In Proc. ACM SIG- COMM, London, England, 1994. Google ScholarDigital Library
- 13.J. Rexford, S. Sen, J. Dey, W. Feng, J. Kurose, J. Stankovic, and D. Towsley. Online smoothing of live, variable-bit-rate video. In Proc. International Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 249-257, May 1997.Google ScholarCross Ref
- 14.J. Rexford and D. Towsley. Smoothing variable-bit-rate video in an internetwork. IEEE/ACM Transactions on Networking, pages 202-215, Apr. 1999. Google ScholarDigital Library
- 15.J. Salehi, Z. Zhang, J. Kurose, and D. Towsley. Supporting stored video: Reducing rate variability and endto-end resource requirements through optimal smoothing. IEEE/ACM Transactions on Networking, 6(4):397--410, Aug. 1998. Google ScholarDigital Library
- 16.S. Sen, J. Rexford, and D. Towsley. Proxy prefix caching for multimedia streams. In Proc. IEEE INFOCOM, Mar. 1999.Google ScholarCross Ref
- 17.The ATM Forum Technical Committee. Traffic management specification version 4.0, Apr. 1996. Available from www. atmforum, com.Google Scholar
- 18.D. E. Wrege, W. Knightly, Zhang, and J. Liebeherr. Deterministic delay bounds for VBR video in packet-switching networks: fundamental limits and practical trade-offs. IEEE/ACM Transactions on Networking, 4(3):352-362, June 1996. Google ScholarDigital Library
- 19.Z.-L. Zhang, S. Nelakuditi, R. Aggarwal, and R. P. Tsang. Efficient selective frame discard algorithms for stored video delivery across resource constrained networks. In Proc. IEEE INFOCOM, Mar. 1999.Google Scholar
- 20.W. Zhao, T. Seth, M. Kim, and M. Willebeek-LeMair. Optimal bandwidth/delay tradeoff for feasible-region-based scalable multimedia scheduling. In Proc. IEEE INFOCOM 98, 1998.Google ScholarCross Ref
Index Terms
- Optimal smoothing schedules for real-time streams (extended abstract)
Recommendations
Optimal smoothing schedules for real-time streams
We consider the problem of smoothing real-time streams (such as video streams), where the goal is to reproduce a variable-bandwidth stream remotely, while minimizing bandwidth cost, space requirement, and playback delay. We focus on lossy schedules, ...
Shared-buffer smoothing of variable bit-rate streams
We study network servers that transmit variable bit-rate streams for real-time playback at remote clients. We introduce an algorithm that removes peaks of disk bandwidth by prefetching stored stream data into the shared buffer space of the server. Using ...
Smoothing Streams in an In-Home Digital Network: Optimization of Bus and Buffer Usage
In an in-home digital network it may be expected that several data streams (audio, video) 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 ...
Comments