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SMIL is perhaps currently the most complete speci cation language for server-side transformation However, there does not yet seem to have been signi cant take up in the device independence arena 8425 COCOON/DELI Section 8414 discussed the CC/PP protocol, which is the current W3C recommendation for device characterisation A Java API has been developed for this protocol as an open source project by SUN, building on work done at HP under the name DELI (Jacobs and Jaj, 2005) This provides a simple programming interface to CC/PP which allows developers to access the capabilities of the current device This has been integrated into COCOON,7 a framework for building web resources using XML as the content source, and using XSLT to transform this into suitable content based on the current device A disadvantage of this approach is the effort required to write suitable XSLT style sheets.



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where n is the number of bit-rate reduction points. Now X (t) cannot be lower than S(t) at the bit-rate reduction points {Ti | i = 0, 1, 2 . . . n}. This implies that the t0 in equation (7.20) cannot be the bit-rate reduction points: t0 = Ti , for i = 1, 2, . . . , n (7.22)

However, as S(t) is constructed with straight lines connecting the bit-rate reduction points, X (t) cannot be lower than S(t) in between two consecutive bit-rate reduction points either: / t0 (Ti 1 , Ti ), for i = 2, 3, . . . , n (7.23)





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8426 WURFL/WALL The Wireless Universal Resource File has been brie y described in Section 8414 One of the most useful features of the WURFL is its hierarchical structure; devices placed at lower nodes in the tree inherit the properties of their ancestors This gives the WURFL a certain degree.

Otherwise X (t) will be convex in the range (Ti 1 , Ti ), which contradicts with the assumption that X (t) has monotonic decreasing rates. From equations (7.21) and (7.22), we conclude that t0 does not exist and the result follows.

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Over the years I had seen at least 20 people fired from Wakeman, Butterman, and Bailey Firings there fell into two categories, much like hurricanes: amicable firings, like when someone had been incompetent but was so good-looking or otherwise likable that human nature demanded it be done respectfully (the type of firing that Craig Taylor would presumably get); and angry firings, where someone always ended up with spittle on his face and his life destroyed because he had put himself in the path of such a disaster Herb was giving me category number two Now he would yell at me until I walked away, then he was going to accuse me of walking away while he was talking to me, and then he was going to tell me to gather my things together and leave He might even throw something.

We evaluate performance of the MDR scheduler and compare it to Optimal Smoothing [1] in this section. To obtain realistic performance results, we collected the video bit-rate traces of 274 different videos from DVD movies for simulation. These are full-length (average 5,781 seconds long and 4,348 MB in size), MPEG-2 encoded videos with an average bit-rate of 6.02 Mbps. The bit-rate varies from below 0.5 Mbps to over 18 Mbps. Long-range (minutes to tens of minutes) bit-rate variations are common in these real-world MPEG-2 encoded videos. We implemented the MDR scheduler presented in Section 7.3.1 in software and used it to compute the transmission schedule for the videos. We also implemented the Optimal Smoothing algorithm [1] for comparison purposes. The generated transmission schedules are then fed into a simulator developed using CNCL [16] to obtain simulation results. The simulation model consists of a system with clients connecting through a 1Gbps backbone network to a server storing the 274 VBR videos. We assume that the backbone network is the bottleneck of the system. For simplicity, we ignore delay and loss in the network. New stream requests are generated according to a Poisson process with various mean inter-arrival times to simulate different system utilization. A new stream request randomly selects a video from the 274-video collection with uniform probability. Note that we adopt the uniform popularity instead of the Zipf popularity model [17] because the video titles have varying bandwidth requirements and lengths. Consequently, using the Zipf popularity will result in large variations in the simulation results, depending on which of the video titles happened to be picked as the hot titles. To obtain more consistent results for comparison, we therefore adopt the uniform popularity model.

http://cocoon.apache.org/

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