Downloads provided by UsageCountsActor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery
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Machumilane A.; Gotta A.; Cassara' P.; Gennaro C.; Amato G.;
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery
Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.
Italy
non-terrestrial networks; satellites; link prediction; reinforcement learning; actor-critic; multipath, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Computer Networks and Communications, Trajectory Optimization, Economics, UAV, Unmanned Aerial Vehicle Communications, Aerospace Engineering, FOS: Mechanical engineering, Redundancy (engineering), Multi-Agent Systems, Real-time computing, Machine Learning (cs.LG), GOE, Computer Science - Networking and Internet Architecture, Engineering, C.2.1, Multipath, Distributed Multi-Agent Coordination and Control, Reinforcement learning, Wireless network, Networking and Internet Architecture (cs.NI), Computer network, GOS, Multipath propagation, Security Challenges in Smart Grid Systems, Computer science, Distributed computing, Actor critic, Multimedia (cs.MM), 68T05, Operating system, Operations management, Multimedia, Control and Systems Engineering, Channel (broadcasting), Computer Science, Physical Sciences, Wireless, Telecommunications, Scheduling (production processes), Computer Science - Multimedia
non-terrestrial networks; satellites; link prediction; reinforcement learning; actor-critic; multipath, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Computer Networks and Communications, Trajectory Optimization, Economics, UAV, Unmanned Aerial Vehicle Communications, Aerospace Engineering, FOS: Mechanical engineering, Redundancy (engineering), Multi-Agent Systems, Real-time computing, Machine Learning (cs.LG), GOE, Computer Science - Networking and Internet Architecture, Engineering, C.2.1, Multipath, Distributed Multi-Agent Coordination and Control, Reinforcement learning, Wireless network, Networking and Internet Architecture (cs.NI), Computer network, GOS, Multipath propagation, Security Challenges in Smart Grid Systems, Computer science, Distributed computing, Actor critic, Multimedia (cs.MM), 68T05, Operating system, Operations management, Multimedia, Control and Systems Engineering, Channel (broadcasting), Computer Science, Physical Sciences, Wireless, Telecommunications, Scheduling (production processes), Computer Science - Multimedia
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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