.4). deadline scheme does not recognize the utility of late The suboptimal performance at high rates is due to the packets and often misses opportunities to schedule iid delay model assumed by the R-D optimization algo- valuable data units close to, or after, their original dead- rithm. With the iid model, policies that specify repeated lines. The R-D optimizing schemes outperform the transmission of a data unit at successive opportunities heuristic schemes regardless of whether the heuristic yield lower calculated error probabilities for errors due to schemes are used with ARD-enabled clients. late loss. The algorithm mistakenly believes that if the data unit is delayed the first time it is transmitted, sub- sequent transmissions may arrive earlier and on time. 3.5.5 Dependent packet delays Thus at higher rates, the algorithm sends packets mul- tiple times even though in our measured trace the loss In the R-D optimized streaming algorithms discussed in probability is very low (0.014%) and packets always Sections 3.5.2, 3.5.3, and 3.5.4, the delays of successive arrive in the order they are transmitted. packets have been modeled as iid shifted-I random Rate-distortion performance can be improved by variables with loss also occurring independently as de- modeling packet delays at successive transmission time scribed in [6]. The iid model simplifies calculations for slots as a first-order Markov random process [10]. In [11] E(), the error probability due to a transmission policy, we have presented an R-D optimization scheme that and for p(), the expected number of transmissions that uses this model. In the scheme, feedback packets inform will result from a transmission policy. It fails to capture the server about the delay over the channel in the recent ARD on, multiple deadlines ARD on, single deadline ARD on, heuristic ARD off, heuristic Markov model IID model prioritized ARQ Rate (kbps) Rate (kbps) Figure 3.5-8 Rate-distortion performance of schedulers for the Figure 3.5-7 Rate-distortion performance of schedule