ent typically employs error tially proposed by Chou and Miao [6]. In the sections that detection and concealment to mitigate the effects of lost follow, we discuss extensions to the framework. Section packets. To adapt to network conditions, the server re- 3.5.3 shows how rich acknowledgments can be in- ceives feedback from the client, for example, as positive corporated. In Section 3.5.4, we discuss the importance of or negative acknowledgments. More sophisticated client multiple deadlines for video packets. Section 3.5.5 dis- feedback might inform about packet delay and jitter, link cusses how the framework can be extended to include speeds, or congestion. a more accurate statistical model for characterizing packet Communications Engineering Desk Reference; ISBN: 9780123746481 Copyright © 2007 Elsevier Inc. All rights reserved CHAPTER 3.5 Network adaptive media transport loss and delay. In Section 3.5.6, finally, we discuss an al- transmission opportunities. These transmission oppor- ternative to rate-distortion optimized streaming that di- tunities are assumed to occur at regular time intervals. rectly minimizes congestion instead of rate. Because of decoding dependencies among data units, the importance of transmitting a packet at a given trans- mission opportunity often depends on which packets will 3.5.2 Rate-distortion optimized be transmitted in the near future. The scheduler there- streaming fore makes transmission decisions based on an entire optimized plan that includes anticipated later trans- missions. Of course, to keep the system practical, only We start by reviewing the seminal work by Chou and a finite time horizon can be considered. Miao on rate-distortion optimized (RaDiO) streaming The plan governing packet transmissions that will [6]. They considered streaming as a stochastic control problem, with the goal of determining which packets occur within a time horizon is called a transmission policy, TT. Assuming a time horizon of N transmission opportu- to send and when to minimize reconstruction 