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We propose a class of rate-distortion optimized packet scheduling algorithms for streaming media by generating a number of nested substreams, with more important streams embedding less important ones in a progressive manner. Our goal is to determine the optimum substream to send at any moment in time, using feedback information from the receiver and statistical characteristics of the video. To do so, we model the streaming system as a queueing system, compute the run-time decoding failure probability of a group of picture in each substream based on effective bandwidth approach, and determine the optimum substream to be sent at that moment in time. We evaluate our scheduling scheme with various video traffic models featuring short-range dependency (SRD), long-range dependency (LRD), and/or multifractal properties. From experiments with real video data, we show that our proposed scheduling scheme outperforms the conventional sequential sending scheme.