In Cluster computing
Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.
Alsmirat Mohammad, Sharrab Yousef, Tarawneh Monther, Al-Shboul Sana’a, Sarhan Nabil
2023-Jan-03
Artificial neural networks, Deep learning, Encoding power consumption modeling, Machine learning, Perceptual video quality modeling, Video communication, Video communication systems, Video streaming
