计算机科学
实时计算
钥匙(锁)
服务器
带宽(计算)
视频跟踪
可靠性
传输(电信)
最优化问题
人工智能
GSM演进的增强数据速率
视频处理
多媒体
计算机网络
计算机安全
电信
算法
软件工程
作者
Wanchun Dou,Xuan Zhao,Xiaochun Yin,Huihui Wang,Yun Luo,Lianyong Qi
标识
DOI:10.1109/tii.2020.3020386
摘要
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.
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