Statistical multiplexing of VBR sources: a matrix-analytic approach
Performance Evaluation - Special issue on performance modeling of high speed telecommunication systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Traffic characterization algorithms for VBR video in multimedia networks
Multimedia Systems
A survey of statistical source models for variable-bit-rate compressed video
Multimedia Systems - Special issue on video content based retrieval
Loss probability calculations and asymptotic analysis for finite buffer multiplexers
IEEE/ACM Transactions on Networking (TON)
Traffic Characterisation and Modelling
BT Technology Journal
Modeling MPEG Scalable Sources
Multimedia Tools and Applications
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Modeling of H.264 high definition video traffic using discrete-time semi-Markov processes
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
On modeling video traffic from multiplexed MPEG-4 videoconference streams
NEW2AN'06 Proceedings of the 6th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
IEEE Communications Surveys & Tutorials
AR-based quadratic modeling for GOP MPEG-encoded video traffic in ATM networks
Computer Communications
Study of the impact of MPEG-1 correlations on video-sources statistical multiplexing
IEEE Journal on Selected Areas in Communications
A general AR-based technique for the generation of arbitrary gamma VBR video traffic in ATM networks
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the Scalable Video Coding Extension of the H.264/AVC Standard
IEEE Transactions on Circuits and Systems for Video Technology
Performance evaluation of a kitting process
ASMTA'11 Proceedings of the 18th international conference on Analytical and stochastic modeling techniques and applications
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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.