 2 ,MN possible reduce the complexity of the basic RaDiO algorithm. policies. Chou and Miao's RaDiO framework [6] over- comes this problem by using conjugate direction search. Their Iterative Sensitivity Adjustment (ISA) algorithm minimizes (3.5.1) with respect to the policy III of one data unit while the transmission policies of other data units are held fixed. Data units' policies are optimized in round-robin order until the Lagrangian cost converges to RaDiO a (local) minimum. Rewritten in terms of the transmission policy of one data unit, (3.5.1), (3.5.2) and (3.5.3) become IL() = (3.5.4) where AB1 S1 incorporates the rate-distortion trade-off multiplier A from (3.5.1), the data unit size B1, and S1, a term that expresses the sensitivity of the overall Rate (kbps) expected distortion to the error probability E( III) of data unit l. The sensitivity term represents the relative im- Figure 3.5-2 PSNR VS. transmitted bit rate for a video streaming portance of a particular data unit and incorporates the system that uses heuristic deadline-constrained prioritized ARQ and for a system that uses RaDiO transmission scheduling. The error probabilities of the other data units that l depends results are for an H.263+ SNR scalable encoding [9] of the on. The sensitivity S changes with each iteration of the Foreman sequence. CHAPTER 3.5 Network adaptive media transport Chou and Sehgal [7] have presented simplified methods a sequence of requests that specify the data units that the to compute approximately optimized policies. The server should transmit. It is straightforward to adapt the framework appears to be robust against simplifications of algorithm discussed in Section 3.4.2.1 to compute a se- the algorithm and approximations to AD1, the in- quence of requests that yield an optimal trade-off be- formation characterizing the value of individual packets tween the expected transmission rate of the media with respect to reconstruction distortion. An attractive packets that the server will send and the expected re- alterna