In recent years, the rapid development of generative AI has made text-driven video prediction models a hot topic in academia and industry. Video prediction should address temporal dynamics and consistency, requiring precise control of scene structures, subject behaviors, camera movements, and semantic expressions. One major challenge is accurately controlling scene dynamics in video prediction to achieve high-quality, semantically consistent outputs. Researchers have proposed key control methods, including camera control, reference video control, semantic enhancement, and subject feature control. These methods aim to improve generation quality, ensuring outputs align with historical context while meeting user needs. This paper systematically explores the core concepts, advantages, limitations, and future directions of these four control approaches.