A Neural Network Based Predictive Mechanism for Available Bandwidth
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
Journal of High Speed Networks
Dynamic bandwidth allocation based on online traffic prediction for real-time MPEG-4 video streams
EURASIP Journal on Applied Signal Processing
On-line prediction of nonstationary variable-bit-rate video traffic
IEEE Transactions on Signal Processing
Real-time network traffic prediction based on a multiscale decomposition
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast
Computer Communications
Cross-layer quality-based resource reservation for scalable multimedia
Computer Communications
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In this paper, we systematically investigate the long-term, online, real-time variable-bit-rate (VBR) video traffic prediction, which is the key and complicated component for advanced predictive dynamic bandwidth control and allocation framework for the future networks and Internet multimedia services. We focus on neural network-based approach for traffic prediction and demonstrate that the prediction performance and robustness of neural network predictors can be significantly improved through multiresolution learning. We show that neural network traffic predictor trained through the multiresolution learning (called multiresolution learning NN traffic predictor) can successfully predict various real-world VBR video traffic up to hundreds of frames in advance, which then lays a solid foundation for predictive dynamic bandwidth control and allocation mechanism. Also, dynamic bandwidth control/allocation based on long-term traffic prediction is discussed in detail.