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However, most of the existing rate control schemes achieve accurate bitrate control at the loss of rate-distortion (R-D) performance. Rate control plays an important role in video coding systems, which makes the output bitrate of a video encoder equal to the target bitrate while minimizing the distortion of the compressed video.
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Simulation results show that the proposed schemes significantly improve the video quality and reduce the transmission latency and energy consumption of the UAV compared with existing schemes. The computational complexity is provided and the optimal utility of the UAV is derived and verified via simulations. A safe RL-based approach is further proposed, which uses deep learning to accelerate the UAV learning process and reduce the video transmission outage probability. This scheme enables the UAV to guarantee the video quality-of-experience (QoE) and reduce the energy consumption without relying on the jamming model or the video service model. More specifically, this scheme applies RL to choose the UAV video compression and transmission policy based on the observed video task priority, the UAV-controller channel state and the received jamming power.
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In this paper, we propose a reinforcement learning (RL)-based UAV anti-jamming video transmission scheme to choose the video compression quantization parameter, the channel coding rate, the modulation and power control strategies against jamming attacks. Unmanned aerial vehicles (UAVs) that are widely utilized for video capturing, processing and transmission have to address jamming attacks with dynamic topology and limited energy.