Statistical Analysis of Video Frame Size Distribution Originating from Scalable Video Codec (SVC)

Designing an effective and high performance network requires an accurate characterization and modeling of network traffic.The modeling of video frame sizes is normally applied in simulation studies and mathematical analysis and generating streams for testing and compliance purposes. Besides, video traffic assumed as a major source of multimedia traffic in future heterogeneous network. Therefore, the statistical distribution of video data can be used as the inputs for performance modeling of networks. The finding of this paper comprises the theoretical definition of distribution which seems to be relevant to the video trace in terms of its statistical properties and finds the best distribution using both the graphical method and the hypothesis test.The data set used in this article consists of layered video traces generating from Scalable Video Codec (SVC) video compression technique of three different movies

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization

A genetic approach to Markovian characterisation of H.264 scalable video

We propose an algorithm for multivariate Markovian characterisation of H.264/SVC scalable video traces at the sub-GoP (Group of Pictures) level. A genetic algorithm yields Markov models with limited state space that accurately capture temporal and inter-layer correlation. Key to our approach is the covariance-based fitness function. In comparison with the classical Expectation Maximisation algorithm, ours is capable of matching the second order statistics more accurately at the cost of less accuracy in matching the histograms of the trace. Moreover, a simulation study shows that our approach outperforms Expectation Maximisation in predicting performance of video streaming in various networking scenarios

The pseudo-self-similar traffic model: application and validation

Since the early 1990¿s, a variety of studies has shown that network traffic, both for local- and wide-area networks, has self-similar properties. This led to new approaches in network traffic modelling because most traditional traffic approaches result in the underestimation of performance measures of interest. Instead of developing completely new traffic models, a number of researchers have proposed to adapt traditional traffic modelling approaches to incorporate aspects of self-similarity. The motivation for doing so is the hope to be able to reuse techniques and tools that have been developed in the past and with which experience has been gained. One such approach for a traffic model that incorporates aspects of self-similarity is the so-called pseudo self-similar traffic model. This model is appealing, as it is easy to understand and easily embedded in Markovian performance evaluation studies. In applying this model in a number of cases, we have perceived various problems which we initially thought were particular to these specific cases. However, we recently have been able to show that these problems are fundamental to the pseudo self-similar traffic model. In this paper we review the pseudo self-similar traffic model and discuss its fundamental shortcomings. As far as we know, this is the first paper that discusses these shortcomings formally. We also report on ongoing work to overcome some of these problems

Catalog Dynamics: Impact of Content Publishing and Perishing on the Performance of a LRU Cache

The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective perishing time. In this paper, we propose a new model for the arrival of content requests, which takes into account the dynamical nature of the content catalog. Based on two large traffic traces collected on the Orange network, we use the semi-experimental method and determine invariants of the content request process. This allows us to define a simple mathematical model for content requests; by extending the so-called "Che approximation", we then compute the performance of a LRU cache fed with such a request process, expressed by its hit ratio. We numerically validate the good accuracy of our model by comparison to trace-based simulation.Comment: 13 Pages, 9 figures. Full version of the article submitted to the ITC 2014 conference. Small corrections in the appendix from the previous versio

A critical look at power law modelling of the Internet

This paper takes a critical look at the usefulness of power law models of the Internet. The twin focuses of the paper are Internet traffic and topology generation. The aim of the paper is twofold. Firstly it summarises the state of the art in power law modelling particularly giving attention to existing open research questions. Secondly it provides insight into the failings of such models and where progress needs to be made for power law research to feed through to actual improvements in network performance.Comment: To appear Computer Communication