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Moreover, the timing of the bit-rate adjustments is no longer critical as video data transmission will not be affected by a later-than-expected downward bit-rate adjustment Intuitively, one will expect the MDR scheduler to require more client buffer as video data are transmitted more aggressively than other smoothing algorithms Using real-world VBR video bit-rate traces, we quantify the trade-off and show that for some video streams, the buffer requirement is indeed increased when compared to smoothing algorithms To tackle this problem, we develop an Aggregated Monotonic Decreasing Rate (AMDR) scheduler to enable one to control the buffer requirement to the same level as smoothing algorithms Surprisingly, simulation results show that the AMDR scheduler can achieve performance comparable to existing smoothing algorithms even when equipped with the same buffer requirement.



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Thus, using the AMDR scheduler, one can provide performance guarantee in streaming VBR videos over mixed-traf c networks with no trade-off in terms of admission complexity, network utilization, client waiting time, and client buffer requirement..

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As discussed in Section 7.2, the fundamental limitation of existing smoothing algorithms is the need for upward rate adjustments, of which correct operation depends on the successful and timely completion of network resource reservations. To remove this limitation, we can use only downward rate adjustments in the transmission schedule. In other words, the initial transmission rate will be the highest, with each subsequent rate lower than the previous one. We call this algorithm the monotonic decreasing rate (MDR) scheduler for obvious reasons. In this chapter, we focus on prerecorded videos. The MDR schedule for a video is computed of ine and is stored with the video for use during video streaming. We present an algorithm to compute the MDR schedule in the next section and derive several properties of the algorithm in Section 7.3.2 to Section 7.3.4.

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Compared to existing smoothing algorithms, the MDR property introduces an additional constraint only downward rate adjustments can be used. Note that although this reduces the set of possible schedules within the feasible region, it still does not uniquely determine the transmission schedule for a given video. In fact, there are still an in nite number of possible MDR schedules within the feasible region. To select a MDR schedule, we need to consider the resultant resource requirements. The choice of the MDR schedule can affect the peak transmission rate and the client buffer requirement, both of which should be minimized. Interestingly, it turns out that we can always compute a MDR schedule that has minimum peak rate and minimum client buffer requirement, among all possible MDR schedules. We de ne a MDR transmission schedule with the set of rate-time tuples: {ri , Ti |i = 1, 2, . . . , n}, where ri and Ti are the transmission rate and commencing time for the ith segment in the transmission schedule as depicted in Figure 7.2; and n is the total number of segments in the MDR transmission schedule. For a MDR transmission schedule, the rates will be monotonic decreasing, i.e., ri > r j , for all i, j where n j > i 1. To compute the schedule, we begin from the origin as depicted in Figure 7.2, and assign the rst segment with the highest transmission rate, i.e., r1 = max A(t) A(0) t > 0 t (7.5)

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and mark the time, denoted by T1 , at which the rate is maximized. The tuple {r1 , T1 } then represents the rst segment of the MDR transmission schedule. Next, we repeat this process with T1 as the starting point to obtain {r2 , T2 }, and so on. In general, the transmission rate for the next segment can be computed from ri+1 = max A(t) A(Ti ) t > Ti t Ti (7.6)

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