Tykhonova O. Conveyor-modular method of multimedia flows integration with delay control in packet based telecommunication networks

Українська версія

Thesis for the degree of Candidate of Sciences (CSc)

State registration number

0419U005431

Applicant for

Specialization

  • 05.12.02 - Телекомунікаційні системи та мережі

04-12-2019

Specialized Academic Board

Д 41.816.01

O.S. Popov Odessa National Academy of Telecommunications

Essay

The object of research is the process of multimedia data transfer in telecommunication networks. The subject of research is multimedia flows integration methods for real-time applications in packet-based telecommunication networks. Methods of research. The methods of system analysis, the theory of information and communication networks were used for development of multimedia flows integration method. Synthesis of the open network mathematical model, as well as optimization of flows distribution, applied the graph theory, matrix and tensor analysis. The computer simulation methods used to research the multimedia flows integration algorithms. Theoretical and practical results. Firstly, the method of the group streaming aggregation of multimedia data in a telecommunication channel developed which accelerates the transfer of streaming data and reduces the amount of data overhead. The method of packet transfer the multimedia data enhanced by dividing the initial aggregated flow into conveyor modules; due to this, the data delays of individual real-time flows are limited depending on the individual quality of service requirements for each flow. Firstly developed the method for calculating and distributing the maximum flow on an open freely oriented network graph, which differs from the existing ones in three open poles, as well as in flexibly redistributed weight of the edges in forward and backward direction; due to this the network performance increased while dynamic communication channels configuration. The flows tensor model enhanced for an open three-pole network by supplementing the network conductivity tensor with the flows generators tensor, due to this network dynamic adaptation ensured while external information load.

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