Abstract: The emergence of smart edge-network content item hotspots,which are equipped with huge storage space (e.g.,several GBs),opens up the opportunity to study the possibility of delivering videos at the edge network.Different from both the conventional content item delivery network (CDN) and the peer-to-peer (P2P) scheme,this new delivery paradigm,namely edge video CDN,requires up to millions of edge hotspots located at users' homes/offices to be coordinately managed to serve mobile video content item.Specifically,two challenges are involved in building edge video CDN,including how edge content item hotspots should be organized to serve users,and how content items should be replicated to them at different locations to serve users.To address these challenges,we propose our data-driven design as follows.First,we formulate an edge region partition problem to jointly maximize the quality experienced by users and minimize the replication cost,which is NP-hard in nature,and we design a Voronoi-like partition algorithm to generate optimal service cells.Second,to replicate content items to edge-network content item hotspots,we propose an edge request prediction based replication strategy,which carries out the replication in a server peak offloading manner.We implement our design and use trace-driven experiments to verify its effectiveness.Compared with conventional centralized CDN and popularity-based replication,our design can significantly improve users' quality of experience,in terms of users' perceived bandwidth and latency,up to 40%.