Traffic Characterization and Modeling for Video Streaming Over Multi-Hop WLANs
ABSTRACT Real-time media streaming in wireless network has seen increased demand on the Internet in recent years, and has drawn tremendous attention from both academia and industry as well. Preliminary traffic measurements demonstrate that data traffic in wireless communication also exhibits self-similarity. However, little research on wireless media streaming is seen in publication. The purpose of this paper is to characterize video streaming traffic over multi-hop wireless local area networks. We begin with an investigation of the video streaming data generated using NS simulation for two typical network scenarios. On this basis, we proposed a new gamma distribution based wavelet model for wireless video traffic. Our mathematical analyses show that this model can capture all traffic characteristics of wireless video streaming, including traffic arrival and self-similarity.
- SourceAvailable from: Zafer Sahinoglu[Show abstract] [Hide abstract]
ABSTRACT: The main objective in telecommunications network engineering is to have as many happy users as possible. In other words, the network engineer has to resolve the trade-off between capacity and QoS requirements. Accurate modeling of the offered traffic load is the first step in optimizing resource allocation algorithms such that provision of services complies with the QoS constraints while maintaining maximum capacity. As broadband multimedia services became popular, they necessitated new traffic models with self-similar characteristics. We present a survey of the self-similarity phenomenon observed in multimedia traffic and its implications on network performance. Our current research aims to fill the gap between this new traffic model and network engineering. An immediate consequence of this study is the demonstration of the limitations or validity of conventional resource allocation methods in the presence of self-similar trafficIEEE Communications Magazine 02/1999; · 3.66 Impact Factor
Conference Paper: Modeling video traffic in the wavelet domain[Show abstract] [Hide abstract]
ABSTRACT: A significant discovery from this work is that although video traffic has complicated short- and long-range dependence in the time domain, the corresponding wavelet coefficients are no longer long-range dependent in the wavelet domain. Therefore, a “short-range” dependent process can be used to model video traffic in the wavelet domain. In this work, we develop such wavelet models for VBR video traffic. The strength of the developed wavelet models includes: (1) it provides a unified approach to model both long-range and short-range dependence in video traffic simultaneously, (2) it has the ability to reduce the temporal dependence so significantly that the wavelet coefficients can be modeled by either independent or Markov models, and (3) the model results in a computationally efficient method on generating high quality video trafficINFOCOM '98. Seventeenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE; 01/1998
Article: Fractional differencing[Show abstract] [Hide abstract]
ABSTRACT: The family of autoregressive integrated moving-average processes, widely used in time series analysis, is generalized by permitting the degree of differencing to take fractional values. The fractional differencing operator is defined as an infinite binomial series expansion in powers of the backward-shift operator. Fractionally differenced processes exhibit long-term persistence and antipersistence; the dependence between observations a long time span apart decays much more slowly with time span than is the case with the more commonly studied time series models. Long-term persistent processes have applications in economics and hydrology; compared to existing models of long-term persistence, the family of models introduced here offers much greater flexibility in the simultaneous modelling of the short-term and long-term behaviour of a time series.Biometrika 01/1981; · 1.65 Impact Factor