Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
EURASIP Journal on Applied Signal Processing
Dynamic bandwidth allocation based on online traffic prediction for real-time MPEG-4 video streams
EURASIP Journal on Applied Signal Processing
A study on the network traffic of Connexion by Boeing: Modeling with artificial neural networks
Engineering Applications of Artificial Intelligence
Neural Networks
Prediction of internet traffic based on Elman neural network
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Load forecasting model based on amendment of mamdani fuzzy system
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Prediction of MPEG video source traffic using bilinear recurrent neural networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Multiscale bilinear recurrent neural network for prediction of MPEG video traffic
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
On-line prediction of nonstationary variable-bit-rate video traffic
IEEE Transactions on Signal Processing
Supervised neural fuzzy schemes in video transmission over Bluetooth
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Neural network based feedback scheduler for networked control system with flexible workload
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Multiscale bilinear recurrent neural network with an adaptive learning algorithm
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Feed Forward Bandwidth Indication (FFBI): Cooperation for an accurate bandwidth forecast
Computer Communications
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Predicting traffic generated by multimedia sources is needed for effective dynamic bandwidth allocation and for multimedia quality-of-service (QoS) control strategies implemented at the network edges. The time-series representing frame or visual object plane (VOP) sizes of an MPEG-coded stream is extremely noisy, and it has very long-range time dependencies. This paper provides an approach for developing MPEG-coded real-time video traffic predictors for use in single-step (SS) and multistep (MS) prediction horizons. The designed SS predictor consists of one recurrent network for I-VOPs and two feedforward networks for P- and B-VOPs, respectively. These are used for single-frame-ahead prediction. A moving average of the frame or VOP sizes time-series is generated from the individual frame sizes and used for both SS and MS prediction. The resulting MS predictor is based on recurrent networks, and it is used to perform two-step-ahead and four-step-ahead prediction, corresponding to multistep prediction horizons of 1 and 2 s, respectively. All of the predictors are designed using a segment of a single MPEG-4 video stream, and they are tested for accuracy on complete video streams with a variety of quantization levels, coded with both MPEG-1 and MPEG-4. Comparisons with SS prediction results of MPEG-1 coded video traces from the recent literature are presented. No similar results are available for prediction of MPEG-4 coded video traces and for MS prediction. These are considered unique contributions of this research.