A new approach for the prediction of end-to-end performance of multimedia streams

This paper proposes a new and accurate way of predicting the end-to-end performance of a multimedia stream. It consists of coupling an approach we have previously developed, which is able to capture very precisely the way humans assess an arriving stream, with classical performance evaluation models. The former approach can automatically quantify the quality of the connection as humans would do (using statistical learning tools), considering this quantitative measure of quality as a function of measurable parameters of the network and the source; the latter allows mapping this "ultimate" quality value to input parameters such as offered traffic or loads, through, for instance, queuing models. In the paper, we also propose a simplification of the global approach dealing with simple closed formulas. We illustrate our approach with a simple case study of unidirectional VoIP performance.

[1]  Henning Sanneck,et al.  A Framework Model for Packet Loss Metrics Based on Loss Runlengths in Proceedings of the SPIE/ACM , 2000 .

[2]  C. Lambrecht.  Perceptual models and architectures for video coding applications , 1996 .

[3]  Donald F. Towsley,et al.  Adaptive FEC-based error control for Internet telephony , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[4]  Peter G. Harrison,et al.  G-networks: new queueing models with additional control capabilities , 1995, SIGMETRICS '95/PERFORMANCE '95.

[5]  Erol Gelenbe,et al.  Stability of the Random Neural Network Model , 1990, Neural Computation.

[6]  Eitan Altman,et al.  Queueing analysis of simple FEC schemes for IP telephony , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[7]  S. Voran,et al.  Estimation of perceived speech quality using measuring normalizing blocks , 1997, 1997 IEEE Workshop on Speech Coding for Telecommunications Proceedings. Back to Basics: Attacking Fundamental Problems in Speech Coding.

[8]  Gerardo Rubino,et al.  A method for quantitative evaluation of audio quality over packet networks and its comparison with existing techniques (in MESAQUIN'04, Prague, June 2004) , 2004 .

[9]  Olof Hagsand,et al.  Dimensioning links for IP telephony , 2001 .

[10]  S. Voran.  The development of objective video quality measures that emulate human perception , 1991, IEEE Global Telecommunications Conference GLOBECOM '91: Countdown to the New Millennium. Conference Record.

[11]  Gerardo Rubino,et al.  Real-Time Video Quality Assessment in Packet Networks: A Neural Network Model , 2001 .

[12]  Timothy A. Hall.  Objective speech quality measures for Internet telephony , 2001, SPIE ITCom.

[13]  Margaret H. Pinson,et al.  On the impact of policing and rate guarantees in DiffServ networks: a video streaming application perspective , 2001, SIGCOMM 2001.

[14]  Jean C. Bolot,et al.  The Case for FEC-based Error Control for Packet Audio in the Internet , 1997 .

[15]  Erol Gelenbe,et al.  Random Neural Networks with Negative and Positive Signals and Product Form Solution , 1989, Neural Computation.

[16]  David Hands,et al.  A Study of the Impact of Network Loss and Burst Size on Video Streaming Quality and Acceptability , 1999, IDMS.

[17]  A. Rix.  Advances in objective quality assessment of speech over analogue and packet based networks , 1999 .

[18]  Christian J. Van Den Branden Lambrecht.  Color moving pictures quality metric , 1996, ICIP.

[19]  Erol Gelenbe,et al.  Learning in the multiple class random neural network , 2002, IEEE Trans. Neural Networks.

[20]  Gerardo Rubino,et al.  A study of real-time packet video quality using random neural networks , 2002, IEEE Trans. Circuits Syst. Video Technol..

[21]  Gerardo Rubino,et al.  Performance evaluation of real-time speech through a packet network: a random neural networks-based approach , 2004, Perform. Evaluation.

[22]  Donald F. Towsley,et al.  Measurement and modelling of the temporal dependence in packet loss , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[23]  Eitan Altman,et al.  On the Utility of FEC Mechanisms for Audio Applications , 2001, QofIS.

[24]  Wonho Yang,et al.  Enhanced modified bark spectral distortion (embsd): an objective speech quality measure based on audible distortion and cognition model , 1999 .