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into the server buffer at its playback rate and injected into the network at the rate that its congestion control algorithm allows. Therefore, when the server buffer builds up, it infers that the available network bandwidth drops and thus reduces the video bit-rate to decrease the server buffer occupancy. Its use of transcoding to control the video bit-rate and server-side information to decide the appropriate video bit-rate makes it readily deployable on existing streaming platforms. Cuetos et al. [1, 2] proposed an adaptive rate control for streaming Fine-Grained Scalable (FGS) video. It adapts the video by controlling the video bit-rate of the enhancement layer of the FGS-coded video based on the client buffer occupancy and network bandwidth information. The heuristic rate control algorithm aims to maintain the client buffer occupancy at around the target level, while at the same time minimizing the video bit-rate variation introduced by adaptation. Although the control algorithm was originally designed to work with FGScoded video, it can also be applied to non-FGS videos using transcoding techniques. The only limitation is the need to con gure a system control parameter which can substantially affect the algorithm s performance. Our results show that the optimal value of the parameter can vary considerably over a wide range under different network traf c patterns (cf. Section 8.6) and so it may be dif cult to optimize the algorithm for use in the Internet, where network traf c patterns are generally very dif cult to predict.



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An example summary obtained using this approach is given in Figure 22 This can t under the ontology learning scenario 4 in Section 25 Ontology construction from a collection of news stories based on named entities: Given a collection of news stories, represent it as a collection of graphs, where the nodes are named entities extracted from the text and relationships between them are based on the context and collocation of the named entities These are further used for visualization of news stories in an interactive browsing environment (Grobelnik and Mladenic, 2004) An example output of the proposed approach is given in Figure 23 This can t under the ontology learning scenario 4 in Section 25 More information on ontology learning from text can be found in a collection of papers (Buitelaar et al.





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In this chapter we consider a video streaming system that streams pre-encoded video data using TCP as the network transport to the receiver for playback Despite the shortcomings of TCP in the context of media streaming (cf Section 61), due to its aggressive congestion control and enforced error-recovery, it does possess a number of appealing features First, TCP is intrinsically TCP-friendly and thus fairness with other TCP traf cs is automatically guaranteed Second, using TCP the sender can stream video using, say, the standard HTTP protocol to the client As most, if not all, video players in the market support HTTP-based video streaming and playback, compatibility is greatly enhanced Third, for security reasons, many companies and ISP block or throttle UDP traf c at their rewalls and gateways, thus making UDP-based video streaming impossible.

And we couldn t even get to that horrible subject until I could tell her why I lost my job Maybe I would get the chance to tell her about the quintiplegic and the wheelchair..

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By contrast, TCP/HTTP streaming can pass through rewalls in the same way as web traf c Finally, to perform bandwidth estimation, the sender will need some form of feedbacks from the client If we use the UDP transport, then the client will need to be modi ed to send explicit feedbacks to the sender to enable bandwidth estimation so that rate adaptation can be performed By contrast, TCP with its built-in ow control already can provide implicit feedbacks to the sender and thus no modi cation to the client is necessary Again, this will greatly enhance the compatibility of the rate-adaptation algorithm to the existing video player software Nevertheless, the rate-adaptation algorithm presented in this study can also be applied to UDP-based video streaming with appropriate support from the client s player software, such as streaming over RTP and using RTCP to report reception statistics back to the server.

, 2005) addressing three perspectives: methodologies that have been proposed to automatically extract information from texts, evaluation methods de ning procedures and metrics for a quantitative evaluation of the ontology learning task, and application scenarios that make ontology learning a challenging area in the context of real applications..

Figure 81 shows the key components in the video streaming system Assume the video data are encoded at a constant bit-rate of rmax bps The rate controller can convert the encoded video to any bit-rate between rmax and rmin (eg, using scalable video coding [7] or transcoding [8 10]) Note that there is a lower limit rmin on the achievable video bit-rate in the model, for example, the bit-rate of the base layer in FGS encoded video [7] or the lowest achievable bit-rate in transcoding [8 10] (cf Section 25)..

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