Abstract: Computing power network connects computing nodes through the network to break the limitation of single point of computing power, and it is rapidly developed and applied in more and more business fields in recent years. The popular live video broadcasting relies on the transmission and transcoding of a large number of video frames, and it is of great practical importance to explore computing power networks for efficient video distribution. Compared with the traditional large-scale data processing, video applications have higher requirements for transmission delay and bandwidth guarantee. However, the computing power of nodes provided by each cloud service varies, and the state of network links between nodes often varies. Therefore, it is a great challenge to realize low latency and high bandwidth video distribution by selecting nodes with the best combined transmission and transcoding performance. Therefore, we design an efficient video distribution scheme based on heterogeneous computing nodes, including planning video transmission paths and reasonably selecting transcoding nodes through reinforcement learning, using priority queuing scheduling for different video distribution tasks and adaptively adjusting node resources to reduce resource bursty competition, adopting a layered-log-synchronization fault tolerance mechanism to quickly restore data consistency after node failures, and finally deploying multi-cloud service distributed nodes to realize a complete video distribution system. A large number of live ultra-high definition video experiments show that the performance of this scheme is significantly improved compared with existing video distribution methods.