Deploying deep neural network (DNN) models on resource-constrained edge devices for real-time video analytics poses significant challenges due to the high resource demands of these models. Current edge-based video analytics approaches often overlook optimizing deep learning models and GPU resource allocations in multi-tenant scenarios. In this paper, we present JSAS-MTMGS, a collaborative video analytics system employing three innovative design strategies. First, we propose a novel video configuration optimization space based on a joint DNN model sharing and splitting scheme to balance computational loads for collaborative processing. This approach reduces network transmission data volume and alleviates resource contention. Second, we design a GPU resource allocation scheme that combines GPU batching with spatial sharing to optimize GPU utilization and increase system throughput, all without relying on costly offline latency collection. Finally, we define the configuration optimization problem alongside GPU allocation as a convex problem and apply convex optimization to make scheduling decisions dynamically. Our experiments demonstrate that JSAS-MTMGS has the best service quality among all compared algorithms.