Addressing the practical difficulties which exist in movie and television design and video making-where early idea forming is done quickly, later-stage alterations are very numerous, and information is easy to become wrong between different stages-this paper defines AI-aided content optimization as a cross-stage content problem of decision making instead of a simple comparison of model effects. Based on a review of relevant research on storyboarding, editing semantics, long-form video understanding, controllable generation, and video quality evaluation , we construct a phased evidence corpus tailored to the production workflow and propose a five-dimensional structured coding framework comprising narrative alignment, shot grammar, temporal coherence, perceived quality, and deployment usability.By integrating composite scores, stage-specific adaptation scores, and dimensional gap coefficients, we conduct a unified comparison of the support capabilities of different technical approaches across script design, shot composition, editing and sequencing, quality review, and delivery deployment.The results indicate that current technological strengths are primarily concentrated in front-end visualization and mid-stage continuous content generation, while back-end quality feedback and delivery deployment remain structural weaknesses. Generation and LongVideo are closer to the core of cross-stage collaboration, Story board shows clear superiority in the early period of design, and Quality mainly undertakes back end diagnosis work. This research puts forward that, the future key point of AI-aided movie and TV making ought to move from enlarging single ability to building a closed cycle system that includes quality feedback, lens improvement, and arrangement interfaces.
