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Communication resource allocation and multi-DNN inference optimization in edge computing-aided video analytics
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AI
DOI：10.1016/j.comcom.2026.108607.png)
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Abstract
En 中文
Video inference will be a building block in many of the envisioned mobile smart systems in 6G networks. Mobile devices will produce video frames that will be analyzed in real-time to enhance users’ perception of the physical world or determine actions to be taken by mobile autonomous robots and smart systems. However, video inference on mobile devices is challenging due to the devices’ limited resources, whereas inference on cloud servers is daunting due to constrained uplink bandwidth. Therefore, communication and processing resources must be efficiently managed and allocated jointly to meet stringent video inference requirements in smart mobile systems. In this paper, we propose a cross-layer deep reinforcement learning (DRL) framework that jointly optimizes the offloading rate at mobile devices, the allocation of uplink physical resource blocks (PRBs), and the orchestration of multi-DNN video frame inference on edge servers. We devise a stochastic framework that models the dynamics of distributed video frame analytics across devices and edge servers, as well as the dynamic optimization of uplink wireless communication links and GPU sharing for multi-DNN inference at edge servers. Furthermore, we formulate the joint optimization of wireless uplink communication and edge compute resources as a Markov decision problem and propose a recurrent deep reinforcement learning framework to optimize communication and computation resource allocation under varying uplink channel quality, demand for computation resource, and video frame inference latency and accuracy requirements. Numerical results show that the proposed framework improves inference accuracy and robustness while maintaining real-time latency and outperforming baseline solutions.
Keywords:
Mobile video analytics
Edge computing
Deep reinforcement learning
Resource allocation
Wireless networks
Multi-DNN inference
Journal
IF:
4.3
Papers:
547
Citations:
1.1W