he Markov decision process when rich acknowledgments are used. CHAPTER 3.5 Network adaptive media transport P-frame itself. Thus there are several deadlines associ- ated with the P-frame, each with its own associated distortion reduction [18]. Another example where a data unit may be associated with multiple deadlines is the case of decoders that allow Accelerated Retroactive Decoding (ARD). This idea was initially proposed in the context of MPEG-2 trans- Conv. ACK mission over ATM [8]. ARD makes use of the ability of Rich ACK many streaming clients to decode video faster than real time. With ARD, when late-arriving data units finally do arrive, the decoder goes back to the frames corre- sponding to the late-arriving packets and quickly again decodes the dependency chain up to the current playout time, but now without error. In this way the remaining Rate (kbps) pictures in the GOP can be decoded and displayed without degradation. Figure 3.5-6 Rich VS. conventional acknowledgments for rate- As shown in [12], the introduction of multiple dead- distortion optimized streaming of QCIF Foreman. lines results in changes to expressions used to calculate expected distortion D() for R-D optimized streaming. Delays for packets not lost were distributed according to For each data unit, there is no longer a single error independent shifted-T distributions with shift K = 50 ms, probability, but a set of them, one for each of the frame mean u = 25 ms, and standard deviation o = 35 ms. deadlines associated with that data unit. This results in In Figure 3.5-6, the rich acknowledgment scheme changes to (3.5.4) that express the Lagrangian cost outperforms the RaDiO scheme with conventional ACKs (3.5.1) as a function of only the transmission policy of for all transmission rates. The maximum PSNR im- one data unit III while other policies are held fixed. With provement is 1.3 dB at a transmitted bit rate of 70 kbps. multiple deadlines, the expression in (3.5.4) becomes The improved performance of the rich acknowledgment scheme is du