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With the emergence of various CNN-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimize the CNN model on edge servers and deploy the optimized model on devices in edge computing environment. However, most of them only optimize the resource consumption within or across models solely, whereas neglecting the other side. In this paper, we propose a novel CNNbased Resource Optimization APProach (CroApp) that not only optimizes the resource consumption within the CNN model but also pays attention to resource optimization across the applications. Specifically, we adopt model compression as the "innermodel" optimization method, as well as computation sharing as the "inter-model" optimization method. Firstly, during "innermodel" optimization, CroApp prunes unnecessary parameters within model on edge servers to reduce the scale of the model. Then, during "inter-model" optimization, CroApp trains a set of shareable models based on the pruned model and sends these shareable models to end devices. Finally, CroApp adaptively adjusts the shared models to reduce resource consumption. The experimental results show that CroApp outperforms the stateof-the-art approaches in terms of resource reduction, scalability, and application performance.

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