Cloud-based video crowdsensing leverages distributed user devices to capture and analyze video data for various applications, ranging from urban monitoring to healthcare. However, optimizing the efficiency and scalability of such systems remains a significant challenge. This paper proposes scalable optimization strategies tailored for cloud-based video crowdsensing environments. We explore techniques to minimize latency, maximize resource utilization, and enhance data reliability through adaptive task allocation and scheduling algorithms. Our approach integrates cloud computing capabilities with edge processing to distribute tasks effectively, leveraging dynamic load balancing and prioritization mechanisms. Experimental evaluations demonstrate significant improvements in system performance metrics, including response time reduction and resource utilization efficiency. The findings highlight the feasibility and benefits of scalable optimization strategies in enhancing the capabilities and practicality of cloud-based video crowdsensing applications.

Cloud-based video crowdsensing, Optimization strategies, Scalability

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