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In this paper, we explore end-to-end loss differentiation algorithms (LDAs) for use with congestion-sensitive video transport protocols for networks with either backbone or last-hop wireless links. As our basic video transport protocol, we use UDP in conjunction with a congestion control mechanism extended with an LDA. For congestion control, we use the TCP-Friendly Rate Control (TFRC) algorithm. We extend TFRC to use an LDA when a connection uses at least one wireless link in the path between the sender and receiver. We then evaluate various LDAs under different wireless network topologies, competing traffic, and fairness scenarios to determine their effectiveness. In addition to evaluating LDAs derived from previous work, we also propose and evaluate a new LDA, ZigZag, and a hybrid LDA, ZBS, that selects among base LDAs depending upon observed network conditions. We evaluate these LDAs via simulation, and find that no single base algorithm performs well across all topologies and competition. However, the hybrid algorithm performs well across topologies and competition, and in some cases exceeds the performance of the best base LDA for a given scenario. All of the LDAs are reasonably fair when competing with TCP, and their fairness among flows using the same LDA depends on the network topology. In general, ZigZag and the hybrid algorithm are the fairest among all LDAs.

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Mean squared error (MSE) and peak signal-to-noiseratio (PSNR) are the most common methods for measuring the quality of compressed images, despite the fact that their inadequacies have long been recognized. Quality for compressed still images is occasionally evaluated using human observers who provide subjective ratings of the images. Both SNR and subjective quality judgments, howevel; may be inappropriate for evaluating progressive compression methods which are to be used for fast browsing applications. In this paper; we present a novel experimental and statistical framework for comparing progressive coders. The comparisons use response time studies in which human observers view a series ofprogressive transmissions, and respond to questions about the images as they become recognizable. We describe the framework and apply it to the comparison of several well known progressive algorithms. ~~ ~ 'This work was supported by the National Science Foundation under Grants MIP-9617366 and MIP-9624729 (CAREER), and by the Center for Wireless Communications at UCSD.