Novel Extended Kalman Filter Using Matrix-Based Levenberg-Marquardt Algorithm and Its Application for Variable Bit-Rate Video Frame-Size Prediction

Publication Date

1-1-2022

Document Type

Conference Proceeding

Publication Title

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

Volume

2022-June

DOI

10.1109/BMSB55706.2022.9828691

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).

Funding Number

LEQSF(2021-22)-RD-A-34

Keywords

Extended Kalman filter (EKF), matrix-based Levenberg-Marquardt algorithm (MLMA), MPEG-4 codec, multimedia network, normalized mean square error (NMSE), video frame-size predictor

Department

Applied Data Science

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