AGiLE: Enhancing Adaptive GOP in Live Video Streaming

被引：0
作者：
Chen, Cheng [1 ]
Yin, Wenpei [2 ]
Huang, Zhexiong [1 ]
Shi, Shu [1 ]
机构：
[1] ByteDance, Beijing, Peoples R China
[2] Shanghai Jiao Tong Univ, Shanghai, Peoples R China
来源：
PROCEEDINGS OF THE 2024 15TH ACM MULTIMEDIA SYSTEMS CONFERENCE 2024, MMSYS 2024 | 2024年
关键词：
Live Streaming; Adaptive GOP; Video Encoding;
D O I：
10.1145/3625468.3647609
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
As live streaming video continues to gain popularity, encoding efficiency remains a critical challenge. Current commercial systems limit the Group of Picture (GOP) length to optimize for spontaneous viewer access, but this often compromises encoding efficiency, especially for popular live streams with intricate background textures and minimal global motion. This paper introduces AGiLE (Adaptive GOP in Live video Encoding), an innovative solution that employs 'pseudo-GOP' to separate encoding efficiency from transmission needs. AGiLE is designed for easy industry adoption and includes a supervised-learning based algorithm for adaptive GOP selection. Our experiments on Douyin's popular live content indicate that AGiLE can reduce bandwidth usage by up to 3.48%, making it a promising solution for the future of live streaming.
引用
收藏
页码：34 / 44
页数：11
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