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In this chapter we consider networks where only best-effort service is supported. This type of network, including the Internet, does not guarantee the delivery of data from the media server to the media client. Moreover, even if the data are successfully transported to the client, the time it takes can vary. The media server has no control over the amount of bandwidth available and can only use whatever is available at the time. Obviously, it will be very challenging to stream high-quality media over such best-effort networks and at the same time maintain playback continuity. As the network provides no guarantees whatsoever, the only alternative is to adapt the media stream to the network bandwidth available. This is made possible by using layered-video codec or media transcoders as discussed in 2. In this chapter, we develop an adaptation algorithm to adjust the output bit-rate of multi-layer-encoded media or transcoders according to the measured available network bandwidth and the estimated client buffer occupancy. With this adaptation algorithm, the media server can then adjust the bit-rate of the media stream dynamically (e.g., reducing bit-rate when available bandwidth drops) so that playback continuity at the client can be improved.



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The lack of end-to-end quality-of-service (QoS) support in today s Internet has caused signi cant dif culties for the deployment of media streaming services such as video broadcasting and video-on-demand. In particular, when the network becomes congested, signi cant packet losses will arise, leading to corrupted or even dropped video frames. Given QoS support is unlikely to be widely available in the near future, researchers have resorted to another approach to tackle this problem. Speci cally, a number of pioneering researchers have investigated algorithms to adapt the video bit-rate to the network bandwidth available [1 6]. For example, when the network becomes congested, the sender will reduce the bit-rate of the encoded video to alleviate the congestion. Clearly, reducing the bit-rate will also degrade the visual quality. Nevertheless, reducing the video bit-rate in a controlled manner at





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Most of the work presented there addresses one of the following problems/ tasks: Extending the existing ontology: Given an existing ontology with concepts and relations (commonly used is the English lexical ontology WordNet), the goal is to extend that ontology using some text, for example Web documents are used in (Agirre et al, 2000) This can t under the ontology learning scenario 5 in Section 25..

the sender will result in far better visual quality than attempting to recover from data loss at the receiver To perform video adaptation we must tackle two fundamental challenges First, the sender must be able to dynamically control or convert the video bit-rate to the desired value This can be accomplished by means of scalable video coding [7] and transcoding [8 10] as discussed in 2 Second, an adaptation algorithm is needed to estimate the network bandwidth available, and subsequently determine the bit-rate to be used for converting and transmitting the video stream This chapter focuses on the second challenge, ie, design of the rate adaptation algorithm This problem has been studied by a number of researchers, including the studies by Rejaie et al.

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Something like this: MY WIFE: But why Why did they fire you ME: I told you that I don t know It might have something to do with the handicapped person MY WIFE: What handicapped person ME: The firm is suing a handicapped person a quintiplegic Rwandan refugee and I sneaked a peek at the files while I was scanning them into the system We are suing this poor man for $20,000 because he hit Herbie Johnson right in the crotch with his wheelchair The firm has seven attorneys working on it [I wipe a single tear from my eye] MY WIFE: How terrible! How could you stand for that ME: Well, I didn t I copied the entire case file, with some very damaging evidence that proves that Herbie was not in fact fitted with a prosthetic you-know-what That medical report is the smoking gun document in the case.

[4] and Assun ao and Ghanbari [5] who adopted UDP as the network transport; and c the studies by Cuetos and Ross [1], Cuetos et al [2], and Jacobs and Eleftheriadis [3] which adopted TCP as the network transport A common property of these adaptation algorithms is the existence of a con gurable operating parameter [1 2], which is typically used in the feedback loop of the algorithms Not surprisingly, as will be illustrated in Section 86, the choice of this operation parameter can signi cantly affect the performance of the rate adaptation algorithm Unfortunately, to optimize this parameter for the best performance will require a priori knowledge of the available network bandwidth over the entire duration of the video session This is clearly not possible in practice and thus poses signi cant dif culties to deploying these rate adaptation algorithms.

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