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To meet the user demand for high-speed and low-latency video services, this paper envisions a cooperative caching and video transcoding architecture in the multi-access edge computing (MEC)-enabled heterogeneous network. Under this architecture, we propose a novel knowledge graph (KG)-based video caching scheme. Specifically, KG reveals the relation between videos, which acts as an external knowledge to reflect user preferences thus guiding caching decisions. The goal of this paper is to minimize the service delay within the caching and computing resource constraints. To this end, we combine KG with the deep reinforcement learning (DRL) method and design a KG-deep Q network (DQN) based caching algorithm. KG is designed to select the related videos as candidate actions for DQN's caching decision. This way improves the convergence performance of DRL while providing rich external …