ple dependency graph is shown for SNR-scalable video encoding with Intra (I), J() = D() + XR(), (3.5.1) Predicted (P), and Bidirectionally predicted (B) frames where the expected transmission rate (Figure 3.5-1). Each square represents a data unit and the arrows indicate the order in which data units can be (3.5.2) decoded. The RaDiO framework can be used to choose an op- timal set of data units to transmit at successive and the expected reconstruction distortion Figure 3.5-1 A directed acyclic graph captures the decoding dependencies for an SNR-scalable encoding of video with L-frames, P-frames, and B-frames. Squares represent data units and arrows indicate decoding order. Network adaptive media transport CHAPTER 3.5 D() = (3.5.3) ISA algorithm to take into account the optimized policy for the other data units. Figure 3.5-2 demonstrates improved video streaming The Lagrange multiplier A controls the trade-off between performance achieved with RaDiO. Luminance PSNR rate and distortion. In (3.5.3) Do is the distortion if no versus transmitted bit rate is plotted for streaming sim- data units arrive, ADI is the distortion reduction if data ulations using an H.263+two-layer SNR scalable unit l arrives on time and can be decoded, and the encoding of the Foreman sequence. The frame rate is 10 product term II/ <1(1 - E( ) is the probability for this fps; a Group of Pictures (GOP) consists of one I-frame to occur. The notation l' is shorthand for the set of followed by nine P-frames. The encoded source rate is 32 data units that must be present to decode data unit 1. kbps for the base layer alone and 69 kbps when the en- In the aforementioned formulation, delays and losses hancement layer is added. The results are for a simulated experienced by packets transmitted over the network are channel in which packet losses occur independently with assumed to be statistically independent. Packet loss is a loss rate of 20%, and packet delays are drawn as in- typically modeled as Bernoulli with some probability, and dependent, shi