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Figure 1.19 Reducing the peak bit-rate by skipping the transmission of some media data (a solution with trade-off in media quality)

(4-9)

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media bit-rate variability can be reduced very effectively. We will revisit this topic in 7 and investigate the streaming of variable bit-rate media in mixed-traf c networks.

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So far we have assumed that the media server must send all media data to the media client. If we remove this constraint, i.e., allowing some media data to be skipped, then we can also lower the network bandwidth required in delivering the media stream. This is illustrated in Figure 1.19 where some of the media data in the segment with peak bit-rate are not transmitted. With incomplete media data, however, the media client obviously will not be able to reconstruct the original compressed media stream for playback. The amount of quality degradation incurred depends heavily on the media encoding algorithm employed, the data skipping algorithm used, the amount of data skipped, as well as the type of the skipped data (e.g., headers, video data, audio data, etc.). In addition to simply skipping data for transmission, another approach is to dynamically reshape the media segments to a lower bit-rate before transmission. For example, knowing that the sixth media segment in Figure 1.19 requires a bit-rate higher than the network bandwidth available, the media server could re-encode the media segment to a lower bit-rate that is within bandwidth limit. This re-encoding can be done, for example, by decoding the media segment and then re-encoding it at a lower bit-rate (by discarding more information); or it can be done using a media transcoder than can reduce the media bit-rate without going through the complete decoding/encoding processes, resulting in less quality degradation and possibly lower processing complexity as well. We further explore media transcoding in Section 2.5 and adaptive streaming in 8.

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Its value ISE = ISE(t0 + TMAX) at the end of a simulation run can be a useful control-system performance measure To see clearly how ISE depends on a system parameter, say the servo damping coefficient r, we may want to plot a graph of ISE versus r 1 An n-run repeated-run study (Section 4-2a) makes n simulation runs with n parameter values of r = r0, r0 + DELr, r0 + 2 DELr, and produces a corresponding ISE value [Eq (4-9)] at the end of each run If a runtime time-history display is not needed, the experiment-protocol script can crossplot ISE versus r as successive runs proceed:

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The nal trade-off to be discussed is complexity. In multimedia streaming media data are almost always compressed before transmitted over the network. Thus the choice of compression

for i = 1 to n r = r + (i 1) * DELr drun plot r, ISE, c | -- (c = 1, 2, is a graph color) reset next

algorithm used will affect the resultant media bit-rate. Take video compression as an example. There are many video compression algorithms available, such as MPEG2, MPEG4, and H.264. In terms of compression ef ciency, i.e., achievable compression ratio at a given video quality level, it is generally agreed that MPEG2 < MPEG4 < H.264. However, the more ef cient video codec such as H.264 also demands substantially more computations in both the encoding and decoding processes. Thus, choosing a more ef cient compression algorithm such as H.264 will lower the network bandwidth required but will increase the computation complexity at both the encoder and decoder. Note that for stored media the encoder complexity is less of an issue as the encoding process can be performed of ine. However, if the media stream is encoded from a live source in realtime, then the encoding complexity will become a signi cant constraint. Decoding, on the other hand, is usually less complex than the encoder and thus presents less of an issue in the choice of codec in a media streaming system.

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