Paper
19 February 2024 Efficient distribution solution for VBR encoded video
Haoran Wang, Xingjun Wang
Author Affiliations +
Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 1306316 (2024) https://doi.org/10.1117/12.3021608
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China
Abstract
The popularity of the internet and the development of mobile internet has led to a rapid growth in internet users and smart devices and a rapid increase in internet traffic consumption[1]. Through an in-depth study of user video clicks and VBR encoded video[2] bit rates, we propose a new and effective push scheme. Based on the storage and channel configuration conditions in the hybrid network, as well as the VBR-encoded video characteristics, a push formula is given for video split, single programme and multiple programmes; with the goal of saving on-demand traffic and ensuring user storage prerequisites, the problem is dynamically planned for the rotation and push scheme selection problem for video split, single programme and multiple programmes. In this paper, the efficient content distribution scheme is based on the premise of guaranteeing memory storage, the goal of saving traffic, and the comparative reference quantities of push, rotation distribution and storage benefits. The dynamic planning problem is analyzed and optimized to a 0-1 backpack problem to find the push and rotation slice numbers and quantities. By improving the efficiency of Internet video content distribution and reducing distribution traffic, the efficient content distribution solution can effectively alleviate network congestion and the rapid growth of CDN investment, thus improving user experience.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Haoran Wang and Xingjun Wang "Efficient distribution solution for VBR encoded video", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 1306316 (19 February 2024); https://doi.org/10.1117/12.3021608
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