TES Modeling of Video Traffic

Benjamin MELAMED; Bhaskar SENGUPTA

TES Modeling of Video Traffic

Benjamin MELAMED, Bhaskar SENGUPTA

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Video service is slated to be a major application of emerging high-speed communications networks of the future. In particular, full-motion video is designed to take advantage of the high bandwidths that will become affordably available with the advent of B-ISDN. A salient feature of compressed video sources is that they give rise to autocorrelated traffic streams, which are difficult to model with traditional modeling techniques. In this paper, we describe a new methodology, called TES (Transform-Expand-Sample) , for modeling general autocorrelated time series, and we apply it to traffic modeling of compressed video. The main characteristic of this methodology is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of an empirical sample such as traffic measurements. Furthermore, the empirical marginal (histogram) and leading autocorrelations are captured simultaneously. Practical TES modeling is computationally intensive and is effectively carried out with software support. A computerized modeling environment, called TEStool, is briefly reviewed. TEStool supports a heuristic search approach for fitting a TES model to empirical time series. Finally, we exemplify our approach by two examples of TES video source models, constructed from empirical codec bitrate measurements: one at the frame level and the other at the group-of-block level. The examples demonstrate the efficacy of the TES modeling methodology and the TEStool modeling environment.

Publication: IEICE TRANSACTIONS on Communications Vol.E75-B No.12 pp.1292-1300

Publication Date: 1992/12/25

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Type of Manuscript: Special Section PAPER (Special Issue on Teletraffic)

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Benjamin MELAMED, Bhaskar SENGUPTA, "TES Modeling of Video Traffic" in IEICE TRANSACTIONS on Communications, vol. E75-B, no. 12, pp. 1292-1300, December 1992, doi: .
Abstract: Video service is slated to be a major application of emerging high-speed communications networks of the future. In particular, full-motion video is designed to take advantage of the high bandwidths that will become affordably available with the advent of B-ISDN. A salient feature of compressed video sources is that they give rise to autocorrelated traffic streams, which are difficult to model with traditional modeling techniques. In this paper, we describe a new methodology, called TES (Transform-Expand-Sample) , for modeling general autocorrelated time series, and we apply it to traffic modeling of compressed video. The main characteristic of this methodology is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of an empirical sample such as traffic measurements. Furthermore, the empirical marginal (histogram) and leading autocorrelations are captured simultaneously. Practical TES modeling is computationally intensive and is effectively carried out with software support. A computerized modeling environment, called TEStool, is briefly reviewed. TEStool supports a heuristic search approach for fitting a TES model to empirical time series. Finally, we exemplify our approach by two examples of TES video source models, constructed from empirical codec bitrate measurements: one at the frame level and the other at the group-of-block level. The examples demonstrate the efficacy of the TES modeling methodology and the TEStool modeling environment.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e75-b_12_1292/_p

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@ARTICLE{e75-b_12_1292,
author={Benjamin MELAMED, Bhaskar SENGUPTA, },
journal={IEICE TRANSACTIONS on Communications},
title={TES Modeling of Video Traffic},
year={1992},
volume={E75-B},
number={12},
pages={1292-1300},
abstract={Video service is slated to be a major application of emerging high-speed communications networks of the future. In particular, full-motion video is designed to take advantage of the high bandwidths that will become affordably available with the advent of B-ISDN. A salient feature of compressed video sources is that they give rise to autocorrelated traffic streams, which are difficult to model with traditional modeling techniques. In this paper, we describe a new methodology, called TES (Transform-Expand-Sample) , for modeling general autocorrelated time series, and we apply it to traffic modeling of compressed video. The main characteristic of this methodology is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of an empirical sample such as traffic measurements. Furthermore, the empirical marginal (histogram) and leading autocorrelations are captured simultaneously. Practical TES modeling is computationally intensive and is effectively carried out with software support. A computerized modeling environment, called TEStool, is briefly reviewed. TEStool supports a heuristic search approach for fitting a TES model to empirical time series. Finally, we exemplify our approach by two examples of TES video source models, constructed from empirical codec bitrate measurements: one at the frame level and the other at the group-of-block level. The examples demonstrate the efficacy of the TES modeling methodology and the TEStool modeling environment.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - TES Modeling of Video Traffic
T2 - IEICE TRANSACTIONS on Communications
SP - 1292
EP - 1300
AU - Benjamin MELAMED
AU - Bhaskar SENGUPTA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E75-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 1992
AB - Video service is slated to be a major application of emerging high-speed communications networks of the future. In particular, full-motion video is designed to take advantage of the high bandwidths that will become affordably available with the advent of B-ISDN. A salient feature of compressed video sources is that they give rise to autocorrelated traffic streams, which are difficult to model with traditional modeling techniques. In this paper, we describe a new methodology, called TES (Transform-Expand-Sample) , for modeling general autocorrelated time series, and we apply it to traffic modeling of compressed video. The main characteristic of this methodology is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of an empirical sample such as traffic measurements. Furthermore, the empirical marginal (histogram) and leading autocorrelations are captured simultaneously. Practical TES modeling is computationally intensive and is effectively carried out with software support. A computerized modeling environment, called TEStool, is briefly reviewed. TEStool supports a heuristic search approach for fitting a TES model to empirical time series. Finally, we exemplify our approach by two examples of TES video source models, constructed from empirical codec bitrate measurements: one at the frame level and the other at the group-of-block level. The examples demonstrate the efficacy of the TES modeling methodology and the TEStool modeling environment.
ER -