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Open-loop architectures, also known as periodic broadcasting, have xed schedules for all media streaming channels irrespective of the user arrival pattern. A new user will receive media data from one or more of the pre-scheduled multicast channels to sustain continuous playback. This implies that the server load is constant regardless of the number of concurrent users in the system. Therefore as long as the network can support it, in principle, there is no limit to how many users an open-loop multicast streaming algorithm can support. In the past decade researchers have developed many innovative open-loop multicast streaming algorithms. Some examples are the Pyramid Broadcasting scheme proposed by Viswanathan and Imielinski [1], the Skyscraper Broadcasting scheme proposed by Hua and Sheu [2], the Greedy Disk-Conserving Broadcasting scheme proposed by Gao et al. [3], the Staircase Data Broadcasting scheme proposed by Juhn and Tseng [4], the Harmonic Broadcasting scheme proposed by Juhn and Tseng [5], the Poly-harmonic Broadcasting scheme proposed by Paris et al. [6], the Pagoda Broadcasting scheme proposed by Paris et al. [7], and many others [8 11]. Interested readers are referred to the study by Hu [12] for a comprehensive study of the existing periodic broadcasting schemes.

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In the next section, we rst describe a taxonomy to classify the design dimensions of openloop multicast streaming algorithms, and in Section 18.3 we present a known performance bound of open-loop algorithms in general. To further illustrate the design choices and performance trade-offs, we present in Section 18.4 the design of the Consonant Broadcasting algorithm. In addition, we also address some practical issues in deploying open-loop algorithms in a multicast-enabled network (e.g., using IP multicast) and present some experimental results obtained from a system implementation.

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Fundamental to all periodic broadcasting schemes are four design dimensions. First, a media stream is divided into a number of smaller segments according to a data partition scheme. Second, the system (i.e., server and network) bandwidth is divided into a number of logical channels according to a bandwidth partition scheme. Third, a predetermined and xed broadcasting schedule de nes when the server should broadcast (or multicast, we will use these two terms interchangeably in the rest of the chapter) a media segment over which logical channels. Fourth, a client reception schedule de nes when a client should receive media data from which logical channels. Different designs of the four design dimensions result in different trade-offs between the three system resources, namely, system bandwidth, client access bandwidth, and client buffer requirement. Clever designs of the four design dimensions can result in signi cant resource savings compared to current unicast-based video streaming systems. More importantly, the resource requirements and performances of these periodic broadcasting systems are independent of the system scale. In other words, the same system can potentially serve an unlimited number of concurrent users, as long as the network infrastructure can accommodate them. This property is instrumental to deploying metropolitan-scale media streaming services as it reduces the per-user system cost when more users are added, thereby allowing the service provider to achieve the crucial economy of scale. In this section, we review some of the existing periodic broadcasting schemes [2 4, 6, 7], and present some known performance bounds. Due to space limitations, interested readers are referred to the study by Hu [12] for a more comprehensive study and comparison of the existing periodic broadcasting schemes. We summarize in Table 18.1 the notations used throughout this chapter. Note also that we use broadcast and multicast interchangeably in this chapter.

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