Abstract: In order to improve the video quality, reduce the number of video jams, and improve the video transcoding rate, a new personalized distribution algorithm of VR short video content based on artificial neural network is proposed. BP neural network is used to compress the original video and determine the execution mode and cache location of VR short video cache file; Transcoding cached high bit rate VR short video stream to generate low bit rate video stream to meet the needs of different network bandwidth; Build a video distribution model, and build a multicast distribution tree based on this model, that is, add some relay servers to minimize the video distribution cost; Finally, through the algorithm of minimizing the distribution cost of VR short video, the bandwidth loss and response delay are effectively reduced to achieve the goal of minimizing the distribution cost. The experimental results show that the video blocking times of this method are always less than six times, which effectively reduces the video blocking times. The PSNR value is high, the increase is large, up to 0.5, and the video transcoding rate is improved, up to 92%.

Han Zhong, Donghyuk Choi, Wonho Choi and Yu Zheng, “Design of Personalized VR Short Video Content Distribution Algorithm based on Artificial Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131203

@article{Zhong2022,
title = {Design of Personalized VR Short Video Content Distribution Algorithm based on Artificial Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131203},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131203},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Han Zhong and Donghyuk Choi and Wonho Choi and Yu Zheng}
}