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Scalable Video Streaming over the Internet

The objectives of this thesis are to investigate the challenges on video streaming, to explore and compare different video streaming mechanisms, and to develop video streaming algorithms that maximize visual quality. To achieve these objectives, we first investigate scalable video multicasting schemes by comparing layered video multicasting with replicated stream video multicasting. Even though it has been generally accepted that layered video multicasting is superior to replicated stream multicasting, this assumption is not based on a systematic and quantitative comparison. We argue that there are indeed scenarios where replicated stream multicasting is the preferred approach.

We also consider the problem of providing perceptually good quality of layered VBR video. This problem is challenging, because the dynamic behavior of the Internet's available bandwidth makes it difficult to provide good quality. Also a video encoded to provide a consistent quality exhibits significant data rate variability. We are, therefore, faced with the problem of accommodating the mismatch between the available bandwidth variability and the data rate variability of the encoded video. We propose an optimal quality adaptation algorithm that minimizes quality variation while at the same time increasing the utilization of the available bandwidth.

Finally, we investigate the transmission control protocol (TCP) for a transport layer protocol in streaming packetized media data. Our approach is to model a video streaming system and derive relationships under which the system employing the TCP protocol achieves desired performance. Both simulation results and the Internet experimental results validate this model and demonstrate the buffering delay requirements achieve desired video quality with high accuracy. Based on the relationships, we also develop realtime estimation algorithms of playout buffer requirements.
Date10 January 2005
CreatorsKim, Taehyun
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Detected LanguageEnglish
Format1376997 bytes, application/pdf

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