Real-time multimedia applications have gained immense popularity in the industrial Internet of Things (IIoT) paradigm. Due to the impact of the complex industrial environment, the transmission of video streaming is usually unstable. In the duration of a low bandwidth transmission, existing optimization methods often reduce the original resolution of some frames in a random way to avoid the video interruption. If the key frames with some important content are selected to be transmitted with a low resolution, it will greatly reduce the effect of industrial supervision. In view of this challenge, a real-time video streaming optimization method by reducing the number of video frames transmitted in the IIoT environment is proposed. Concretely, a deep learning-based object detection algorithm is recruited to effectively select the key frames in our method. The key frames with the original resolution will be transmitted along with audio data. As some nonkey frames are selectively discarded, it is helpful for smooth network transmitting with fewer bandwidth requirements. Moreover, we employ edge servers to run the object detection algorithm, and adjust video transmission flexibly. Extensive experiments are conducted to validate the effectiveness, and dependability of our method.

中文翻译：

---

在IIoT中启用边缘计算的深度学习用于实时视频优化

实时多媒体应用在工业物联网（IIoT）范例中获得了极大的普及。由于复杂的工业环境的影响，视频流的传输通常是不稳定的。在低带宽传输期间，现有的优化方法通常会以随机方式降低某些帧的原始分辨率，以避免视频中断。如果选择一些重要内容的关键帧进行低分辨率的传输，将大大降低产业监督的效果。鉴于这一挑战，提出了一种通过减少在IIoT环境中传输的视频帧的数量的实时视频流优化方法。具体来说，我们采用了一种基于深度学习的目标检测算法，以有效地选择关键帧。具有原始分辨率的关键帧将与音频数据一起传输。由于某些非关键帧被有选择地丢弃，因此有助于以较少的带宽需求进行平滑的网络传输。此外，我们采用边缘服务器来运行目标检测算法，并灵活地调整视频传输。进行了广泛的实验以验证我们方法的有效性和可靠性。并灵活调整视频传输。进行了广泛的实验以验证我们方法的有效性和可靠性。并灵活调整视频传输。进行了广泛的实验以验证我们方法的有效性和可靠性。