QoE Optimization for Semantic Self-Correcting Video Transmission in Multi-UAV Networks

2025-07-09

AI Summary

To address bandwidth constraints, latency fluctuations, and high packet loss rates in real-time video transmission over multi-UAV networks, this paper proposes SSCV-G, a semantics-aware self-correcting framework. SSCV-G integrates ultra-fine-grained bitrate control, multi-frame joint semantic decoding, and a semantic-indexed self-correction mechanism to significantly improve bandwidth efficiency and packet-loss resilience. Furthermore, it innovatively introduces Multi-User Proximal Policy Optimization (MUPPO) into semantic video transmission—enabling, for the first time, end-to-end joint optimization of communication resources and semantic bitrate. Experimental results demonstrate that SSCV-G consistently outperforms conventional codecs (H.264, H.265, AV1) in coding efficiency, bandwidth adaptability, and packet-loss recovery capability. Under dynamic network conditions, MUPPO-driven Quality-of-Experience (QoE) optimization sustains superior performance over existing baselines. This work establishes a deployable semantic communication paradigm tailored for time-sensitive air–ground–space integrated applications.

Technology Category

• Computer Vision: Multi-modal Vision
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Optimize QoE for real-time UAV video streaming in dynamic networks
• Address bandwidth limitations and packet loss in video transmission
• Enhance semantic video coding efficiency and robustness

Innovation

• Semantic codebook encoding for bandwidth efficiency
• ST-ViT transformer reconstructs dropped frames
• MUPPO optimizes QoE via reinforcement learning