Variable-Bit-Rate Video Frame-Size Prediction by the Extended Kalman Filter Using Levenberg-Marquardt Algorithm

Publication Date

3-1-2023

Document Type

Article

Publication Title

IEEE Transactions on Broadcasting

Volume

69

Issue

1

DOI

10.1109/TBC.2022.3185461

First Page

75

Last Page

84

Abstract

It is crucial to dynamically predict the future frame-sizes (bit-rates) for multimedia networking. All of the conventional bit-rate predictors are based on the assumption that instantaneous bit-rates are known precisely all the time (in the absence of uncertainty) which is surely not realistic in practice. In this work, we propose a new expectation-maximization (EM) based extended Kalman filter (EKF) to predict the bit-rates, where the EKF state-transition models will be optimized by the Levenberg-Marquardt algorithm (LMA). The main advantages of our proposed novel EKF-based bit-rate prediction approach are given as follows. First, our proposed EKF-based predictor can optimally estimate the bit-rates in the presence of uncertainty and/or noise. Second, our proposed novel EKF-based bit-rate prediction approach does not require a separate classifier to determine the individual frame-types as the conventional approach so our approach would be more robust than the conventional approach. Numerical evaluation of bit-rate (frame-size) prediction is also conducted over three movies encoded by the MPEG-4 standard. Compared to the existing Kalman-filter based bit-rate prediction methods, our proposed new LMA-EKF predictor can achieve much better performance in terms of the normalized mean square error (NMSE) and the inverse of signal-to-noise-ratio (SNR).

Keywords

expectation-maximization (EM) algorithm, Extended Kalman filter (EKF), Levenberg-Marquardt algorithm (LMA), MPEG-4, multimedia network, variable-bit-rate (VBR) video streaming

Department

Applied Data Science

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