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0 0 10 20 30 40 50 Trace No. 60 70 80 90 100
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Figure 8.6a Rebuffering ratio for AVS with and without preemptive rate control
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800000 700000 600000 500000 400000 300000 200000 100000 0 0 10 20 30 40 50 Trace No. AVS AVS(no_preempt) 60 70 80 90 100
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Figure 8.6b Average video bit-rate for AVS with and without preemptive rate control
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Figure 8.7a Rebuffering ratios of CR and AVS for different traces
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800000 700000 600000 500000 400000 300000 200000 100000 0 0 10 20 30 40 50 Trace No. AVS CR Avg. CR Optimal 60 70 80 90 100
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Figure 8.7b Average video bit-rates of CR and AVS for different traces
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algorithm gives lower rebuffering ratio in most traces than the average-case performance of CR. Averaging over all 94 traces, the AVS algorithm can achieve 20% lower rebuffering ratio than the CR algorithm, while the average video bit-rate is only 0.7% lower than the CR algorithm, showing that both algorithms can make ef cient use of the network bandwidth. Although the optimal rebuffering ratio of the CR algorithm is much lower, it requires of ine optimization which is impossible in practice. By contrast, the AVS algorithm does not require any a priori knowledge of the network bandwidth available or tuning of any control parameter and thus will be simpler to deploy.
8.7 Summary
In this chapter we presented a rate adaptation algorithm for streaming video over the Internet which only supports best-effort service. The algorithm has two unique features to maximize its compatibility with existing video player software. First, we showed that the rate adaptation algorithm can be applied to streaming video over TCP/HTTP, which is compatible with most of the existing video player software. Second, the rate adaptation algorithm performs network bandwidth and client buffer occupancy estimations using only local information. Thus, explicit feedbacks from the client are not needed and hence existing video player software can be supported. Moreover, the presented algorithm does not need any parameter tuning or a priori knowledge of the available network bandwidth to perform well, thus simplifying the deployment of the adaptation algorithm in practice.
Note
1. Network traces used in the simulations belong to the NLANR project sponsored by the National Science Foundation and its ANIR division under Cooperative Agreement No. ANI-9807479, and the National Laboratory for Applied Network Research.
References
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Adaptive Media Streaming
[9] B.K. Natarajan and B. Vasudev, A Fast Approximate Algorithm for Scaling Down Digital Images in the DCT Domain, Proc. of IEEE Int. Conf. Image Processing, vol. 2, Oct. 1995, pp. 241 243. [10] H. Sun, W. Kwok, and J.W. Zdepski, Architecture for MPEG Compressed Bitstream Scaling, IEEE Tran. of Circuits and Systems for Video Technology, vol. 6, no. 2, April 1996, pp. 191 199. [11] R. Rejaie, M. Handley, and D. Estrin, RAP: An End-to-End Rate-based Congestion Control Mechanism for Realtime Streams in the Internet, Proc. of IEEE INFOCOM, vol. 3, April 1999, pp. 1337 1345. [12] The network simulator ns-2. [Online]. Available: http://www.isi.edu/nsnam/ns/ [13] S. Floyd and V. Paxson, Dif culties in Simulating the Internet, IEEE/ACM Tran. of Networking, vol. 9, no. 4, August 2001, pp. 392 403. [14] S. Floyd and T. Henderson, The NewReno Modi cation to TCP s Fast Recovery Algorithm, RFC 2582, April 1999. [15] NLANR Measurement and Network Analysis Group. [Online]. Available: http://pma.nlanr.net/Traces/long/ bell1.html [16] BellLabs Internet Traf c Research. [Online]. Available: http://cm.bell-labs.com/cm/ms/departments/sia/ InternetTraf c/