Evaluation of Neural Network Architectures for MPEG-4 Video Traffic Prediction

2006 ◽  
Vol 52 (2) ◽  
pp. 184-192 ◽  
Author(s):  
A. Abdennour
Keyword(s):  
Neural Network ◽  
Traffic Prediction ◽  
Network Architectures ◽  
Video Traffic ◽  
Neural Network Architectures ◽  
Video Traffic Prediction

scholarly journals Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction

2017 ◽  
Vol 6 (4) ◽  
pp. 283 ◽  
Author(s):  
J.P. Kharat
Keyword(s):  
Neural Network ◽  
Network Traffic ◽  
Traffic Load ◽  
Traffic Prediction ◽  
Network Architectures ◽  
Nonlinear Prediction ◽  
Video Traffic ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Neural Network Architectures

<p>Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed forward and Time Delay Neural Network. The results show that cascade feed forward network produces minimum error as compared to other networks. This paper also compares the results of traditional prediction method of averaging of frames for future frame prediction with neural based methods. The experimental results show that nonlinear prediction based on NNs is better suited for traffic prediction purposes than linear forecasting models.</p>

Design of a Novel Variable Bit Rate Video Traffic Prediction Model Based on Neural Networks

2014 ◽  
Vol 989-994 ◽  
pp. 4143-4146
Author(s):  
Zuo Zheng Lian ◽  
Hai Zhen Wang ◽  
Da Hui Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Performance ◽  
Traffic Prediction ◽  
Variable Bit Rate ◽  
Video Traffic ◽  
Vbr Video ◽  
Video Traffic Prediction

The video business has gradually become the main business of the network traffic, video traffic is Variable Bit Rate (VBR), which has nonlinear and sudden features, a single prediction model is not fit for describing those features, and prediction accuracy is not high. Because of wavelet analysis has advantages of multi-resolution and dealing with unexpectedness, and the neural network has better nonlinear fitting characteristics, in order to improve the prediction performance, the paper researched on the problem of VBR video traffic prediction based on neural networks, a novel prediction model AMWM is proposed, the model firstly introducing BP neural network to multi-fractal wavelet model, and designing prediction method, which introduced multi-fractal wavelet model to model VBR video traffic, and then applying BP neural network to forecast scale coefficient decomposed, and forecast multiplier using AR model, and predict traffic generated by wavelet reconstruction. Finally, the model is built and simulated. The experimental result shows that prediction performance based AMWM is better compared with multi-fractal wavelet model.

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