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Now if Bni < BT , then it implies that the client buffer occupancy is below the threshold. Hence the server will reduce the video bit-rate to raise the buffer occupancy to BT by substituting Bn n+1 = BT in equation (8.9) to obtain: ri+1 = 1 BT Bni M Di+1 (8.10)
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Otherwise if Bni BT , then it implies that the client buffer occupancy is above the threshold. In this case the server will simply maintain the current client buffer occupancy by setting Bni+1 = Bni in equation (8.9) to obtain ri+1 . This is a conservative strategy to reduce the likelihood of buffer under ow. Thus, we have: ri+1 = Di+1 (8.11)
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Finally, the server checks and limits the computed video bit-rate to the feasible range [rmin , rmax ] by ri+1 = min{rmax , max {rmin , ri+1 }} (8.12)
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Note that in contrast to previous works [1 3], this adaptation algorithm has no control parameter that requires either of ine or online optimization. This has practical signi cance as it is not easy to optimize the control parameters without knowledge of the available network bandwidth.
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8.5.2 Preemptive Rate Control
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In our trace-driven simulations, we nd that the available network bandwidth can occasionally drop drastically to a very low value. These sudden bandwidth drops do not appear to be predictable and thus can result in client video playback starvation. The fundamental problem is that the adaptation algorithm is executed only when a new video segment is to be transmitted. Thus, if bandwidth drops signi cantly, then the transmission of the current video segment will stall. The adaptation algorithm cannot react in this case as the current video segment has not yet been completely transmitted. Meanwhile the client will continue consuming video data for playback and thus may run into buffer under ow. To tackle this problem, we can use a preemptive scheduling technique to shorten the delay for the adaptation algorithm to react to changing network conditions. Instead of waiting inde nitely for a video segment to be completely submitted into the server buffer, the scheduler will timeout after Mri+1 /Di+1 seconds, which is the expected time required to submit the (i + 1)th video segment into the server buffer. If not all video data can be submitted, then the remaining yet-to-be-submitted data will be discarded and the remaining video segment transcoded again according to the new estimates on the client buffer occupancy and the available network bandwidth. Note that preemptive rate control requires the video transcoder to be able to adjust the video bit-rate in between a video segment. The implementation will be highly dependent on the video compression algorithm employed and further study is required to identify the constraints and tradeoffs of this technique.
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8.6 Performance Evaluation
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In this section, we use a trace-driven simulator written in ns-2 [12] to evaluate the performance of the presented adaptation algorithm (denoted by AVS) and compare it with the current stateof-the-art algorithm proposed by Cuetos and Ross [1 2] (denoted by CR). Figure 8.4 depicts the simulated network topology. We use the common NewReno TCP [13 14] as the transport protocol to deliver the video data to the client. Cross-traf c is generated from a packet trace le obtained from Bell Labs [15 16].1 The trace le captured 94 hours of network traf c passing through a rewall. We divide the 94-hour trace le into 94 1-hour trace les and run a simulation for each 1-hour trace le to evaluate the algorithms performance under different cross-traf c scenarios. Both the streaming traf c and the cross-traf c share a link of R Mbps as shown in Figure 8.4. For each simulation, we adjust R so that the network has just suf cient bandwidth to stream the video, i.e., R = rmax + c, where c is the average data rate of the cross traf c. We summarize the system settings in Table 8.1. We use two performance metrics, namely, rebuffering ratio and average video bit-rate, to evaluate the algorithms performance. Rebuffering ratio is de ned as the proportion of frames
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