Method for video data transmission at the application layer with adaptive control in an unmanned aerial vehicles network

Abstract

Video streams transmitted from unmanned aerial vehicles are a critical source of situational information for monitoring, search-and-rescue, emergency response, and security applications. In such time-critical scenarios, the practical value of a video frame rapidly decreases after a deadline, while unmanned aerial vehicle networks are characterized by stochastic variability of delay, jitter, losses, and dynamic topology. Therefore, the problem of ensuring timely delivery of video frames through adaptive, application-layer control becomes relevant. The object of research is the process of video frame transmission in unmanned aerial vehicle networks under stochastic channel and routing conditions. The subject of research is methods and models of adaptive control of video frame delivery at the application layer, including strategy selection under deadline constraints. The purpose of this paper is to develop an application-layer method for adaptive transmission of video frames that increases the probability of delivery before a deadline and reduces the fraction of late-or-lost frames in unmanned aerial vehicle networks. A conceptual framework for adaptive control of video frame transmission is proposed, including a decision model for selecting transmission strategies based on observed network conditions and deadline requirements. An adaptive algorithm for per-frame strategy selection is developed, incorporating rules for retransmission limitation, buffering management, and robustness mechanisms (e.g., redundancy-based delivery). A set of evaluation metrics is defined to characterize timeliness and reliability, including the probability of on-time delivery, average delay of delivered frames, loss probability, and the late-or-lost fraction. Simulation-based assessment demonstrates that adaptive control improves deadline reliability compared to fixed transmission strategies across representative unmanned aerial vehicle network regimes.