Modeling video sources for real-time scheduling
Multimedia Systems
Analysis, modeling and generation of self-similar VBR video traffic
SIGCOMM '94 Proceedings of the conference on Communications architectures, protocols and applications
RCBR: a simple and efficient service for multiple time-scale traffic
IEEE/ACM Transactions on Networking (TON)
Using adaptive linear prediction to support real-time VBR video under RCBR network service model
IEEE/ACM Transactions on Networking (TON)
Self-similar Traffic Prediction Using Least Mean Kurtosis
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
Recursive Non Linear Models for On Line Traffic Prediction of VBR MPEG Coded Video Sources
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
A Fast Non-Linear Adaptive Algorithm for Video Traffic Prediction
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
Real time variable bit rate video traffic prediction: Research Articles
International Journal of Communication Systems
Short-Term MPEG-4 AVC Bandwidth Prediction for Broadband Cable Networks
CNSR '08 Proceedings of the Communication Networks and Services Research Conference
Prediction of MPEG Traffic Data Using a Bilinear Recurrent Neural Network with Adaptive Training
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02
Supporting real time VBR video using dynamic reservation based on linear prediction
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
Prediction of MPEG-coded video source traffic using recurrent neural networks
IEEE Transactions on Signal Processing
IEEE Communications Surveys & Tutorials
Online smoothing of variable-bit-rate streaming video
IEEE Transactions on Multimedia
Dynamic resource allocation via video content and short-termtraffic statistics
IEEE Transactions on Multimedia
Real-time VBR video traffic prediction for dynamic bandwidth allocation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Improving signal prediction performance of neural networks through multiresolution learning approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Asynchronous transfer of video
IEEE Communications Magazine
Bandwidth allocation strategies for transporting variable bit rate video traffic
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
Call admission for prerecorded sources with packet loss
IEEE Journal on Selected Areas in Communications
Packet video and its integration into the network architecture
IEEE Journal on Selected Areas in Communications
Predictive dynamic bandwidth allocation for efficient transport of real-time VBR video over ATM
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
Sparse basis selection: new results and application to adaptive prediction of video source traffic
IEEE Transactions on Neural Networks
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Video bandwidth forecasts can empower video transport mechanisms with a new intelligence that can increase the efficiency of Dynamic Bandwidth Allocation. We exploit the fact that for pre-recorded video, the size of every video frame is known prior to the video being delivered. We propose Feed-Forward Bandwidth Indication (FFBI) which feeds video frame sizes forward in a sequence of video frames. We extend FFBI to live video by introducing a delay at the source equivalent to the forecast window. We compare FFBI to the most accurate forecast methods found in the literature. With network transport of video projected to supplant other transport mechanisms over the next few years, we conduct a performance analysis of FFBI within Ethernet Passive Optical Networks (EPONs). We find that the use of FFBI can provide a 50% reduction in queueing delay compared to the use of no forecasting and a 35% reduction in queueing delay compared to other forecasting methods. In addition, we find that FFBI can provide a very significant reduction in queueing delay variation compared to the use of no forecasting or other forecasting methods.