Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks
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Abstract (arXiv)
This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale. The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2019.
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Citing papers per year: 2021: 5; 2022: 3.
8 papers.
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- 2021Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehiclescited by 1
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- 2022Spatio-Temporal Attack Course-of-Action (COA) Search Learning for Scalable and Time-Varying Networkscited by 0
- 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
- 2021Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles26 Dec 2021
- 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
- 2021Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games21 May 2021