Joint Video Frame Scheduling and Resource Allocation for Device-Edge Collaborative Video Intelligent Analytics
With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse requirements in different immersive service scenarios, especially in accuracy and latency. However, as resource-limited terminal devices struggl...
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| Vydáno v: | IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6 |
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| Jazyk: | angličtina |
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IEEE
24.03.2025
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| ISSN: | 1558-2612 |
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| Abstract | With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse requirements in different immersive service scenarios, especially in accuracy and latency. However, as resource-limited terminal devices struggle to accom-plish high-accuracy video intelligent analytics tasks, video frames have to be offloaded to edge nodes with sufficient computational and cache resources for further processing. Therefore, in this paper, we consider device-edge collaboration video intelligent an-alytics tasks to improve trade-off performance between accuracy and latency. Specifically, we propose a joint optimization scheme for video frame scheduling, adaptive video frame compression and Machine Learning (ML) model caching to maximize the minimum of utility among all users. We divide the joint optimization problem into two sub-problems and use convex optimization to solve the adaptive frame compression optimization problem. Furthermore, to avoid the curse of dimensionality, we design an expert-assisted proximal policy optimization (EPPO)-based joint video frame scheduling and resource allocation algorithm. Simulation results demonstrate the superiority of the proposed scheme in improving video intelligent analytics performance. |
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| AbstractList | With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse requirements in different immersive service scenarios, especially in accuracy and latency. However, as resource-limited terminal devices struggle to accom-plish high-accuracy video intelligent analytics tasks, video frames have to be offloaded to edge nodes with sufficient computational and cache resources for further processing. Therefore, in this paper, we consider device-edge collaboration video intelligent an-alytics tasks to improve trade-off performance between accuracy and latency. Specifically, we propose a joint optimization scheme for video frame scheduling, adaptive video frame compression and Machine Learning (ML) model caching to maximize the minimum of utility among all users. We divide the joint optimization problem into two sub-problems and use convex optimization to solve the adaptive frame compression optimization problem. Furthermore, to avoid the curse of dimensionality, we design an expert-assisted proximal policy optimization (EPPO)-based joint video frame scheduling and resource allocation algorithm. Simulation results demonstrate the superiority of the proposed scheme in improving video intelligent analytics performance. |
| Author | Su, Yi Li, Jiayi Wang, Hui Chi, Xiaoyu Xu, Xiaodong Han, Shujun |
| Author_xml | – sequence: 1 givenname: Jiayi surname: Li fullname: Li, Jiayi email: ljyxxxx@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Beijing,China,100876 – sequence: 2 givenname: Xiaoyu surname: Chi fullname: Chi, Xiaoyu email: chixiaoyu@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Beijing,China,100876 – sequence: 3 givenname: Hui surname: Wang fullname: Wang, Hui email: wanghuijt@migu.chinamobile.com organization: National Engineering Research Center of Mobile Network Technologies §Migu Interactive Entertainment Co., Ltd – sequence: 4 givenname: Yi surname: Su fullname: Su, Yi email: suyi@migu.chinamobile.com organization: National Engineering Research Center of Mobile Network Technologies §Migu Interactive Entertainment Co., Ltd – sequence: 5 givenname: Shujun surname: Han fullname: Han, Shujun email: hanshujun@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Beijing,China,100876 – sequence: 6 givenname: Xiaodong surname: Xu fullname: Xu, Xiaodong email: xuxiaodong@bupt.edu.cn organization: Beijing University of Posts and Telecommunications,Beijing,China,100876 |
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| SubjectTerms | Accuracy Adaptation models Adaptive scheduling adaptive video frame compression Analytical models Collaboration Convex functions Edge intelligence ML model caching Optimization Performance evaluation Resource management Simulation video frame scheduling video intelligent analytics |
| Title | Joint Video Frame Scheduling and Resource Allocation for Device-Edge Collaborative Video Intelligent Analytics |
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