In this letter, we propose a video traffic model based on a class of stochastic processes, which we call truncated GeoY/G/∞ input processes. Group of picture (GOP) size traces are modeled by truncated GeoY/G/∞ input process with gamma-distributed batch sizes Y and Weibull-like autocorrelation function. With full-length MPEG-4 video traces in QCIF, we run simulations to show that our proposed model estimates packet loss ratios at various traffic loads more accurately than existing modeling methods.

We consider wireless interactive data broadcasting environments consisting of the broadcast channel for data dissemination and the communication channels for client requests. Modeling client impatience as the soft deadline of client requests, we propose a broadcast scheduling based on a combination of periodic scheduling and priority-based scheduling. The server partitions data items into hot and cold-item sets according to the optimized cut-off point. We apply periodic and priority-based scheduling to hot and cold item sets, respectively, in order to maximize the average utility of the items. We investigate the optimized cut-off point by analyzing the average utility of items as a function of the cut-off point. Simulation results show that our proposed algorithm outperforms existing methods in various circumstances in terms of average utility as well as average response time.

Future broadband ATM networks are expected to accommodate various kinds of multi-media services with different traffic characteristics and quality of service (QOS) requirements. However, it is very difficult to control traffic by conventional mechanisms in this complex traffic environment. As an alternative approach, a multilayer perceptron neural network model is proposed as an intelligent control mechanism like "a traffic control policeman" in order to perform ATM connection admission control. This proposed neural control model is analyzed by computer simulations in a homogeneous and heterogeneous traffic environment and the result shows the effectiveness of this intelligent control mechanism, compared with that of an analytical method.

Considering digital multimedia broadcasting (DMB) with reverse channels, we propose a novel scheduling algorithm for data dissemination as a combination of push and pull schemes. After collecting statistics of requests from clients, the server partitions the data items into hot and cold sets, according to the number of requests. The broadcast server schedules and broadcasts hot items periodically based on a push algorithm. On an empty slot between hot items scheduled, the server broadcasts a cold item based on an on-demand pull mechanism. Simulations show that our proposed algorithm achieves high successful response ratio with a response time small enough to be practical.