Recently, various adaptationmethods have been proposed to copewith throughput fluctuations inHTTP adaptive streaming (HAS).\nHowever, thesemethods have mostly focused on constant bitrate (CBR) videos.Moreover,most of themare qualitative in the sense\nthat performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for\nstreaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP).With this approach, the system should have\na probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution\nis based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics.We\ndevelop mathematical models to predict and develop simulation models to measure the average performance of the adaptation\npolicy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in\nplanning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality\nand average quality switch.
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