Intelligent Video Caching at Network Edge: A Multi-Agent Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Intelligent Video Caching at Network Edge: A Multi-Agent Deep Reinforcement Learning Approach

Published:2020-07 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
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Author:

Wang Fangxin,Wang Feng,Liu Jiangchuan,Shea Ryan,Sun Lifeng

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9146503/9155217/09155373.pdf?arnumber=9155373

Cited by 102 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Toward High-Quality Low-Latency 360° Video Streaming With Edge–Client Collaborative Caching and Super-Resolution;IEEE Internet of Things Journal;2024-09-01

2. Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks;Future Internet;2024-08-14

3. Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networks;IEEE Transactions on Network and Service Management;2024-08

4. Caching User-Generated Content in Distributed Autonomous Networks via Contextual Bandit;IEEE Transactions on Mobile Computing;2024-08

5. Design of User Behavior-aware Video Chunk Caching Strategy at Network Edge;2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN);2024-07-02

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