Edge computer-vision systems need to satisfy low-latency requirements, even under scarce computation and network resource availability. The novelty of this thesis is the investigation of how admission control, batching, and concurrency need to be jointly designed to jointly maximize the number of task completions without deadline violations. First, we perform an empirical characterization of modern inference frameworks (e.g., PyTorch, NVIDIA TensorRT, YOLO). The findings show that batching and parallelism benefit throughput, but hit diminishing returns as host-side processing saturates. Inspired by this, we present a communication–computation model which subsumes rate-dependent uploads, limited bandwidth, and asynchronous task arrivals in a single compact form.