Video streaming services depend on the underlying communication infrastructure and available network resources to offer ultra-low latency, high-quality content delivery. Open Radio Access Network (ORAN) provides a dynamic, programmable, and flexible RAN architecture that can be configured to support the requirements of time-critical applications. This work considers a setup in which the constrained network resources are supplemented by \gls{GAI} and \gls{MEC} {techniques} in order to reach a satisfactory video quality. Specifically, we implement a novel semantic control channel that enables \gls{MEC} to support low-latency applications by tight coupling among the ORAN xApp, \gls{MEC}, and the control channel. The proposed concepts are experimentally verified with an actual ORAN setup that supports video streaming. The performance evaluation includes the \gls{PSNR} metric and end-to-end latency. Our findings reveal that latency adjustments can yield gains in image \gls{PSNR}, underscoring the trade-off potential for optimized video quality in resource-limited environments.

翻译：视频流媒体服务依赖于底层通信基础设施和可用网络资源，以实现超低延迟、高质量的内容传输。开放无线接入网络（ORAN）提供了一种动态、可编程且灵活的无线接入网架构，可被配置以满足时间敏感型应用的需求。本研究探讨了一种通过生成式人工智能（GAI）与多接入边缘计算（MEC）技术补充受限网络资源的方案，以实现令人满意的视频质量。具体而言，我们实现了一种新颖的语义控制信道，通过ORAN xApp、MEC与控制信道之间的紧密耦合，使MEC能够支持低延迟应用。所提出的概念在一个支持视频传输的实际ORAN实验平台上进行了验证。性能评估包括峰值信噪比（PSNR）指标和端到端延迟。我们的研究结果表明，延迟调整能够带来图像PSNR的提升，这凸显了在资源受限环境下为优化视频质量进行权衡的潜力。