Research of Video Stream Frame Delay in UAVs FPV-Control Information Exchange Channel of HSTNs Space Segment

In this paper, dependence of applied delay and video stream frame loss values of the FPV video stream on the size of the transmitted frames compressed by a neural network codec when implementing information exchange channels between unmanned aerial vehicles and an external pilot station in the space segment of a hybrid orbital-ground communication network. Satellite information exchange channels built on the basis of the Starlink Low-Earth orbit satellite constellations, as well as the Yamal-402 and Yamal-601 geostationary orbits, are considered. Relevance of the work is based on the necessity to achieve a specified level of quality of FPV control services in satellite communication networks.

Methods used. Application delay and frame loss of the video stream using neural network codecs are measured using the field testing method. Video stream frames are segmented, transmitted via the UDP transport protocol and reconstructed. The probability distribution density of delays is reconstructed using the Rosenblatt-Parzen method with a density estimation function with a Gaussian kernel.

Results. Average transmission delays and frame losses of a video stream (compressed by a neural network codec) via satellite communication systems in low-Earth and geostationary orbits are obtained. Distributions of video stream delay dependencies on the payload size are reconstructed. The nature of the distribution of video stream delays compressed by a neural network codec is found. Novelty of the obtained results lies in the study of the nature of video stream delays when implementing the FPV control service through various space segments of a hybrid orbital-ground communication network using neural network video stream compression codecs.

Practical significance. The results can be used in modeling applied satellite information exchange channels for implementing the FPV control service in order to form an optimal configuration of the neural network codecs used.

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