We describe a mechanism for scalable control of multicast continuous media streams. The mechanism uses a novel probing mechanism to solicit feedback information in a scalable manner and to estimate the number of receivers. In addition, it separates the congestion signal from the congestion control algorithm, so as to cope with heterogeneous networks. This mechanism has been implemented in the IVS videoconference system using options within RTP to elicit information about the quality of the video delivered to the receivers. The H.261 coder of IVS then uses this information to adjust its output rate, the goal being to maximize the perceptual quality of the image received at the destinations while minimizing the bandwidth used by the video transmission. We find that our prototype control mechanism is well suited to the Internet environment. Furthermore, it prevents video sources from creating congestion in the Internet. Experiments are underway to investigate how the scalable probing mech...

Contents 6.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 161 6.2 Adaptation algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : 167 6.3 Convergence of the adaptive quantizer : : : : : : : : : : : : : : : : : 179 6.4 Experimental results : : : : : : : : : : : : : : : : : : : : : : : : : : : 186 6.5 Conclusions and future work : : : : : : : : : : : : : : : : : : : : : : : 190 6.1 Introduction The most successful methods for lossless compression of data, such as arithmetic coding [55, 116, 80], Lempel-Ziv coding [120] or dynamic Huffman coding [35, 54, 111], are all adaptive (see [6] for an extensive review of lossless compression). While the initial work on entropy coding (e.g. Huffman coding) relied on knowing, or measuring, the source distribution, adaptive schemes make no prior assumptions on the source statistics, which the coders try to l

xiii 1 Introduction 1 1.1 Signi#cance of the Research ::::::::::::::::::::: 1 1.2 Video Compression and Rate Control :::::::::::::::: 5 1.2.1 MPEG Encoding ::::::::::::::::::::::: 5 1.2.2 Bit-Rate Control ::::::::::::::::::::::: 8 1.3 Outline of the Dissertation :::::::::::::::::::::: 11 1.4 Summary of Contributions :::::::::::::::::::::: 13 2 Bit-Rate Control Techniques 15 2.1 Introduction :::::::::::::::::::::::::::::: 15 2.2 Predictive Bit-Rate Control Scheme :::::::::::::::: 16 2.3 MPEG Test Model 5 Algorithm ::::::::::::::::::: 20 2.3.1 Assumptions on R-D Characteristics :::::::::::: 21 2.3.2 Control Procedures ::::::::::::::::::::: 22 2.3.3 Problems in TM5 Algorithm :::::::::::::::: 26 2.4 Delayed-Decision Bit-Rate Control ::::::::::::::::: 30 2.4.1 Lagrange Multiplier Techniques ::::::::::::::: 31 2.4.2 Trellis-Based Techniques ::::::::::::::::::: 34 2.4.3 Techniques for Dependent Quantization :::::::::: 36 2.5 Approximation Models for Rate and Distortion Functions :::: 39 2...