Deploying CNNs for video analytics on resource-constrained edge nodes demands systematic co-optimization of accuracy, latency, energy, and privacy. A comprehensive methodological framework encompasses both posttraining and training-time compression, alongside principled lightweight architecture design. Uniform, symmetric, and power-of-two quantizers can be formalized, ranging from the techniques of network pruning, filter and channel pruning, knowledge distillation, lightweight design, etc.