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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Published in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC pp. 1 - 6
Main Authors: Li, Jiayi, Chi, Xiaoyu, Wang, Hui, Su, Yi, Han, Shujun, Xu, Xiaodong
Format: Conference Proceeding
Language:English
Published: IEEE 24.03.2025
Subjects:
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
ISSN:1558-2612
Online Access:Get full text
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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.
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
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Snippet With the development of 6G immersive communication, video intelligent analytics has garnered significant attention. Video intelligent analytics has diverse...
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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
URI https://ieeexplore.ieee.org/document/10978266
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