tive to ISA is a randomized algorithm recently construction distortion that will result [7]. Figure 3.4-3 developed by Setton in which heuristically and randomly illustrates the differences between sender-driven and generated candidate policies are compared at each receiver-driven streaming. transmission opportunity [15,17]. The best policy from By combining sender-driven and receiver-driven the previous transmission opportunity is one of the can- techniques, the RaDiO framework can be applied to di- didates and thus past computations are efficiently reused. verse network topologies. For example, RaDiO might be With a performance similar to ISA, the randomized al- implemented in a proxy server placed between the gorithm usually requires much less computation. backbone network and a last hop link (Figure 3.5-4) [3]. The proxy coordinates the communication between the media server and the client using a hybrid of receiver- 3.5.2.2 Receiver-driven streaming and sender-driven streaming. End-to-end performance is improved compared to a sender- or receiver-driven When transmitting many audio and video streams si- RaDiO system because traffic created by retransmissions multaneously, a media server might become computation of media packets lost in the last hop to the client does not limited rather than bandwidth limited. It is therefore need to traverse and congest the backbone link. desirable to shift the computation required for network adaptive media transport from the server to the client to the extent possible. Fortunately, rate-distortion opti- 3.5.3 Rich acknowledgments mized streaming can be performed with the algorithm running at the client SO that very little computation is In one extension to the RaDiO framework, streaming required at the server [7]. performance is improved through the use of rich ac- For receiver-driven streaming, the client is provided knowledgments [4]. In sender-driven RaDiO streaming information about the sizes, distortion reduction values, using conventional acknowledgments, when a clien