Robustness of Time-Division Schedules for Internet Broadcast

The model we consider consists of a server and many clients. The clients have a large incoming bandwidth and little or no outgoing bandwidth. The server repeatedly broadcasts information through the air to the clients. There are two information items with lengths l_1 and l_2, and demand probabilities p_1 and p_2. The demand probability of an item is simply the relative frequency of requests for that item by the clients, scaled such that the sum of the p_i's is 1. These items contain static data. This allows us to receive data out of order and use parts of different broadcasts to reassemble items. The metric we use to evaluate broadcast schedules is expected waiting time. This is the expected time a client must wait for an item, averaged over all items and clients, with weight p_i for item i