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ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems

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Title: ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems
Authors: Badnava, Babak, Chakareski, Jacob, Hashemi, Morteza
Publication Year: 2025
Collection: ArXiv.org (Cornell University Library)
Subject Terms: Information Theory, Machine Learning, Image and Video Processing
Description: Diverse emerging VR applications integrate streaming of high fidelity 360 video content that requires ample amounts of computation and data rate. Scalable 360 video tiling enables having elastic VR computational tasks that can be scaled adaptively in computation and data rate based on the available user and system resources. We integrate scalable 360 video tiling in an edge-client wireless multi-connectivity architecture for joint elastic task computation offloading across multiple VR users called ElasticVR. To balance the trade-offs in communication, computation, energy consumption, and QoE that arise herein, we formulate a constrained QoE and energy optimization problem that integrates the multi-user/multi-connectivity action space with the elasticity of VR computational tasks. The ElasticVR framework introduces two multi-agent deep reinforcement learning solutions, namely CPPG and IPPG. CPPG adopts a centralized training and centralized execution approach to capture the coupling between users' communication and computational demands. This leads to globally coordinated decisions at the cost of increased computational overheads and limited scalability. To address the latter challenges, we also explore an alternative strategy denoted IPPG that adopts a centralized training with decentralized execution paradigm. IPPG leverages shared information and parameter sharing to learn robust policies; however, during execution, each user takes action independently based on its local state information only. The decentralized execution alleviates the communication and computation overhead of centralized decision-making and improves scalability. We show that the ElasticVR framework improves the PSNR by 43.21%, while reducing the response time and energy consumption by 42.35% and 56.83%, respectively, compared with a case where no elasticity is incorporated into VR computations. ; Submitted to ACM TOMM
Document Type: text
Language: unknown
Relation: http://arxiv.org/abs/2512.12366
Availability: http://arxiv.org/abs/2512.12366
Accession Number: edsbas.ECF004D3
Database: BASE
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  – Url: http://arxiv.org/abs/2512.12366#
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  Label: Title
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  Data: ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Badnava%2C+Babak%22">Badnava, Babak</searchLink><br /><searchLink fieldCode="AR" term="%22Chakareski%2C+Jacob%22">Chakareski, Jacob</searchLink><br /><searchLink fieldCode="AR" term="%22Hashemi%2C+Morteza%22">Hashemi, Morteza</searchLink>
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  Data: 2025
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  Data: ArXiv.org (Cornell University Library)
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  Data: <searchLink fieldCode="DE" term="%22Information+Theory%22">Information Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+Learning%22">Machine Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+and+Video+Processing%22">Image and Video Processing</searchLink>
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  Data: Diverse emerging VR applications integrate streaming of high fidelity 360 video content that requires ample amounts of computation and data rate. Scalable 360 video tiling enables having elastic VR computational tasks that can be scaled adaptively in computation and data rate based on the available user and system resources. We integrate scalable 360 video tiling in an edge-client wireless multi-connectivity architecture for joint elastic task computation offloading across multiple VR users called ElasticVR. To balance the trade-offs in communication, computation, energy consumption, and QoE that arise herein, we formulate a constrained QoE and energy optimization problem that integrates the multi-user/multi-connectivity action space with the elasticity of VR computational tasks. The ElasticVR framework introduces two multi-agent deep reinforcement learning solutions, namely CPPG and IPPG. CPPG adopts a centralized training and centralized execution approach to capture the coupling between users' communication and computational demands. This leads to globally coordinated decisions at the cost of increased computational overheads and limited scalability. To address the latter challenges, we also explore an alternative strategy denoted IPPG that adopts a centralized training with decentralized execution paradigm. IPPG leverages shared information and parameter sharing to learn robust policies; however, during execution, each user takes action independently based on its local state information only. The decentralized execution alleviates the communication and computation overhead of centralized decision-making and improves scalability. We show that the ElasticVR framework improves the PSNR by 43.21%, while reducing the response time and energy consumption by 42.35% and 56.83%, respectively, compared with a case where no elasticity is incorporated into VR computations. ; Submitted to ACM TOMM
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      – SubjectFull: Information Theory
        Type: general
      – SubjectFull: Machine Learning
        Type: general
      – SubjectFull: Image and Video Processing
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      – TitleFull: ElasticVR: Elastic Task Computing in Multi-User Multi-Connectivity Wireless Virtual Reality (VR) Systems
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