Novel Extended Kalman Filter Using Matrix-Based Levenberg-Marquardt Algorithm and Its Application for Variable Bit-Rate Video Frame-Size Prediction
Document Type
Conference Proceeding
Publication Date
1-1-2022
Abstract
Dynamic bandwidth allocation based on multimedia network-traffic prediction has been emerging as an important problem in multimedia networks. The well-known Kalman filter has been adopted for such network-traffic prediction but it is assumed that the state-transition model is linear and known a priori. Therefore, it is favorable to extend the conventional linear state-transition model to be nonlinear and dynamically estimate it. It is not trivial to estimate such a nonlinear model especially for a multimedia network supporting the 5G technology and operating in a highly mobile environment. In this work, we would like to address the aforementioned challenges by designing a new matrix-based Levenberg-Marquardt algorithm based extended Kalman filter (MLMA-EKF) to dynamically estimate the video frame-sizes in compiance with MPEG-4 specifications. Numerical results over MPEG-4 encoded movies demonstrate that our proposed novel MLMA-EKF frame-size predictor is effective for predicting the future bit rates, or video frame-sizes, in terms of normalized mean square error (NMSE).
Publication Source (Journal or Book title)
IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB
Recommended Citation
Chang, S., Wu, H., Yan, K., & Wu, Y. (2022). Novel Extended Kalman Filter Using Matrix-Based Levenberg-Marquardt Algorithm and Its Application for Variable Bit-Rate Video Frame-Size Prediction. IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB, 2022-June https://doi.org/10.1109/BMSB55706.2022.9828691