Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
Not on the map. Why?
Abstract (arXiv)
This paper considers one-hop device-to-device (D2D)-assisted wireless caching networks that cache video files of varying quality levels, with the assumption that the base station can control the video quality but cache-enabled devices cannot. Two problems arise in such a caching network: file placement problem and node association problem. This paper suggests a method to cache videos of different qualities, and thus of varying file sizes, by maximizing the sum of video quality measures that users can enjoy. There exists an interesting trade-off between video quality and video diversity, i.e., the ability to provision diverse video files. By caching high-quality files, the cache-enabled devices can provide high-quality video, but cannot cache a variety of files. Conversely, when the device caches various files, it cannot provide a good quality for file-requesting users. In addition, when multiple devices cache the same file but their qualities are different, advanced node association is required for file delivery. This paper proposes a node association algorithm that maximizes time-averaged video quality for multiple users under a playback delay constraint. In this algorithm, we also consider request collision, the situation where several users request files from the same device at the same time, and we propose two ways to cope with the collision: scheduling of one user and non-orthogonal multiple access. Simulation results verify that the proposed caching method and the node association algorithm work reliably.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Jun 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 3; 2023: 1.
7 papers.
- 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
- 2021Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networkscited by 1
- 2021A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimizationcited by 0
- 2022Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networkscited by 0
- 2022Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networkscited by 0
- 2023Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipescited by 0
- 2023Two Tales of Platoon Intelligence for Autonomous Mobility Control: Enabling Deep Learning Recipes19 Jul 2023
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control3 Oct 2022
- 2022Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networks2 Sep 2022
- 2022Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks27 May 2022
- 2021Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks15 Nov 2021
- 2021A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization2 Sep 2021
- 2021Quantum Neural Networks: Concepts, Applications, and Challenges2 Aug 2021