In this thesis, we analyze the bandwidth requirements of MPEG-4 AVC video traffic and then propose and evaluate the accuracy of new MPEG-4 AVC video traffic models. First, we analyze the bandwidth requirements of the videos by comparing the statistical characteristics of the different frame types. We analyze their coefficient of variability, autocorrelation, and crosscorrelation in both short and long term. The Hurst parameter is also used to investigate the long range dependence of the video traces. We then provide an insight into B-frame dropping and its impact on the statistical characteristics of the video trace. This leads us to design two algorithms that predict the size of the B-frame and the size of the group of pictures (GOP) in the short-term. To evaluate the accuracy of the prediction, a model for the error is proposed. In a broadband cable network, B-frame size prediction can be employed by a cable headend to provision video bandwidth efficiently or more importantly, reduce bit rate variability and bandwidth requirements via selective B-frame dropping, thereby minimizing buffering requirements and packet losses at the set top box. It will be shown that the model provides highly accurate prediction, in particular for movies encoded in high quality resolution. The GOP size prediction can be used to provision bandwidth. We then enhance the B-frame and GOP size prediction models using a new scene change detector metric. Finally, we design an algorithm that predicts the size of different frame types in the long-term. Clearly, a long-term prediction algorithm may suffer degraded prediction accuracy and the higher complexity may result in higher latency. However, this is offset by the additional time available for long-term prediction and the need to forecast bandwidth usage well ahead of time in order to minimize packet losses during periods of peak bandwidth demands. We also analyze the impact of the video quality and the video standard on the accuracy of the model.