计算机科学
次梯度方法
GSM演进的增强数据速率
跟踪(心理语言学)
Lyapunov优化
弹道
实时计算
期限(时间)
计算机网络
人工智能
机器学习
Lyapunov重新设计
哲学
语言学
李雅普诺夫指数
物理
天文
混乱的
量子力学
作者
Yili Jin,Junhua Liu,Fangxin Wang
出处
期刊:
日期:2022-03-01
卷期号:: 600-601
被引量:9
标识
DOI:10.1109/vrw55335.2022.00151
摘要
Compared to traditional videos, streaming 360° videos is more difficult. We propose Ebublio, a novel intelligent edge caching framework consisting of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module. The former integrates the features of video content, user trajectory, and other users' records for combined prediction. The latter employs the Lyapunov framework and a subgradient optimization toward the optimal caching replacement policy. Our trace-driven evaluation further demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE under similar traffic consumption.
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