Current Adaptive Bit Rate (ABR) methods react to network congestion after it occurs, causing application layer buffering and latency spikes in live video streaming. We introduce a proactive semantic control channel that enables coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) components for seamless live video streaming between mobile devices. When the transmitting UE experiences poor Uplink (UL) conditions, the MEC proactively instructs downscaling based on low-level RAN metrics, including channel SNR updated every millisecond, preventing buffering before it occurs. A Generative AI (GAI) module at the MEC reconstructs high-quality frames from downscaled video before forwarding to the receiving UE via the typically more robust Downlink (DL). Experimental validation on a live ORAN testbed with 50 video streams shows that our approach reduces latency tail behavior while achieving up to 4 dB improvement in PSNR and 15 points in VMAF compared to reactive ABR methods. The proactive control eliminates latency spikes exceeding 600 ms, demonstrating effective cross-layer coordination for latency-critical live video streaming.

翻译：当前自适应比特率（ABR）方法在网络拥塞发生后才进行响应，导致实时视频流传输中应用层缓冲和延迟尖峰。本文提出一种主动式语义控制信道，能够协调开放无线接入网络（ORAN）xApp、移动边缘计算（MEC）和用户设备（UE）组件，实现移动设备间的无缝实时视频流传输。当发送端UE经历较差的上行链路（UL）条件时，MEC基于包括每毫秒更新的信道信噪比在内的底层RAN指标，主动指导视频降级处理，从而在缓冲发生前进行预防。MEC端的生成式人工智能（GAI）模块在通过通常更稳定的下行链路（DL）转发给接收端UE前，从降级视频中重建高质量帧。在包含50路视频流的实时ORAN测试平台上进行的实验验证表明，相较于响应式ABR方法，我们的方法在降低延迟尾部行为的同时，实现了高达4 dB的峰值信噪比（PSNR）提升和15分的视频多方法评估融合（VMAF）改进。该主动控制机制消除了超过600 ms的延迟尖峰，证明了面向延迟敏感的实时视频流传输的有效跨层协调能力。