Unmanned aerial vehicle (UAV) downlink transmission facilitates critical time-sensitive visual applications but is fundamentally constrained by bandwidth scarcity and dynamic channel impairments. The rapid fluctuation of the air-to-ground (A2G) link creates a regime where reliable transmission slots are intermittent and future channel quality can only be predicted with uncertainty. Conventional deep joint source-channel coding (DeepJSCC) methods transmit coupled feature streams, causing global reconstruction failure when specific time slots experience deep fading. Decoupling semantic content into a deterministic structure component and a stochastic texture component enables differentiated error protection strategies aligned with channel reliability. A predictive transmission framework is developed that utilizes a split-stream variational codec and a channel-aware scheduler to prioritize the delivery of structural layout over reliable slots. Experimental evaluations indicate that this approach achieves a 5.6 dB gain in peak signal-to-noise (SNR) ratio over single-stream baselines and maintains structural fidelity under significant prediction mismatch.

翻译：无人机下行链路传输对关键时效性视觉应用至关重要，但其根本上受限于带宽稀缺与动态信道损伤。空对地链路的快速波动形成了一种传输机制：可靠传输时隙呈间歇性出现，且未来信道质量仅能通过不确定性进行预测。传统的深度联合信源信道编码方法传输耦合的特征流，当特定时隙遭遇深度衰落时会导致全局重建失败。将语义内容解耦为确定性结构分量与随机性纹理分量，可实现与信道可靠性相匹配的差异化差错保护策略。本文提出一种预测性传输框架，该框架采用分流变分编解码器与信道感知调度器，优先在可靠时隙传输结构布局信息。实验评估表明，该方法在峰值信噪比上较单流基线方案获得5.6 dB增益，并在显著预测失配情况下保持结构保真度。