QoE Optimization for Semantic Self-Correcting Video Transmission in Multi-UAV Networks
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
Real-time unmanned aerial vehicle (UAV) video streaming is essential for
time-sensitive applications, including remote surveillance, emergency response,
and environmental monitoring. However, it faces challenges such as limited
bandwidth, latency fluctuations, and high packet loss. To address these issues,
we propose a novel semantic self-correcting video transmission framework with
ultra-fine bitrate granularity (SSCV-G). In SSCV-G, video frames are encoded
into a compact semantic codebook space, and the transmitter adaptively sends a
subset of semantic indices based on bandwidth availability, enabling
fine-grained bitrate control for improved bandwidth efficiency. At the
receiver, a spatio-temporal vision transformer (ST-ViT) performs multi-frame
joint decoding to reconstruct dropped semantic indices by modeling intra- and
inter-frame dependencies. To further improve performance under dynamic network
conditions, we integrate a multi-user proximal policy optimization (MUPPO)
reinforcement learning scheme that jointly optimizes communication resource
allocation and semantic bitrate selection to maximize user Quality of
Experience (QoE). Extensive experiments demonstrate that the proposed SSCV-G
significantly outperforms state-of-the-art video codecs in coding efficiency,
bandwidth adaptability, and packet loss robustness. Moreover, the proposed
MUPPO-based QoE optimization consistently surpasses existing benchmarks.
