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Broadcasting is an efficient and scalable way of transmitting data over wireless channels to an unlimited number of clients. In this chapter the problem of allocating data to multiple channels is studied, assuming flat data scheduling per channel and the presence of unrecoverable channel transmission errors. The behavior of wireless channels is described by the Bernoulli model, in which each packet transmission has the same probability to fail and each transmission error is independent from the others. The objective is that of minimizing the average expected delay experienced by the clients. Optimal solutions can be found in polynomial time when all data items have unit lengths, while heuristics are presented when data items have non-unit lengths. Extensive simulations, performed on benchmarks whose item popularities follow Zipf distributions, show that good sub-optimal solutions are found.

We discuss the broadcasting problem of N data items over K wireless channels, under the assumptions of skewed data allocation to channels and flat data scheduling per channel. Both the uniform and nonuniform length cases are surveyed showing their exact and heuristic solutions, respectively. Two of the heuristic methods are greedy and the third one is based on a dynamic programming procedure developed to solve a simplified version of the problem. An experimental evaluation of our heuristics is presented and the quality of the solutions generated is compared against a lower bound, which is derived by relaxing the problem and then solving it optimally via a dynamic programming procedure developed in earlier sections.