Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A neural network controller for congestion control in ATM multiplexers
Computer Networks and ISDN Systems
Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
Neuro-fuzzy modeling and prediction of VBR MPEG video sources
Real-Time Imaging - Special issue on real-time digital video over multimedia
Prediction of MPEG-coded video source traffic using recurrent neural networks
IEEE Transactions on Signal Processing
Real-time VBR video traffic prediction for dynamic bandwidth allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
VBR video traffic management using a predictor-based architecture
Computer Communications
IEEE Journal on Selected Areas in Communications
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Generalization of adaptive neuro-fuzzy inference systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Two neuro-fuzzy control schemes for a traffic-regulating buffer in wireless technology
Information Sciences: an International Journal
Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast
Computer Communications
Neuro-Fuzzy approach to video transmission over ZigBee
Neurocomputing
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Multimedia traffic and particularly MPEG-coded video streams are growing to be a major traffic component in high-speed networks. Accurate prediction of such traffic enhances the reliable operation and the quality of service of these networks through a more effective bandwidth allocation and better control strategies. However, MPEG video traffic is characterized by a periodic correlation structure, a highly complex bit rate distribution and very noisy streams. Therefore, it is considered an intractable problem. This paper presents a neuro-fuzzy short-term predictor for MPEG-4-coded videos. The predictor is based on the Adaptive Network Fuzzy Inference System (ANFIS) to perform single-step predictions for the I, P and B frames. Short-term predictions are also examined using smoothed signals of the video sequences. The ANFIS prediction results are evaluated using long entertainment and broadcast video sequences and compared to those obtained using a linear predictor. ANFIS is capable of providing accurate prediction and has the added advantage of being simple to design and to implement.