Social video caching

We investigate online browsing of interrelated content, represented as a catalog of items of interest featuring graph dependencies. The content is served to clients via a system of decentralized proxy caches connected to cloud servers. A client selects the next item to browse from the list of recommended items, displayed on the currently browsed items catalog page. A cache has a limited size to have every item selected by its browsing clients available for local access. Thus, the system pays a penalty, whenever a client selects an item that cannot be served directly from the proxy. Conversely, the system gains a reward, if a client selects an immediately available item. We aim to select the items to cache that maximize the profit earned by the system, for the given cache capacity. We design two linear-time optimization techniques for finding the desired items to cache. We enhance the operation of the system via two additional strategies. The first one dynamically tracks the items selection probabilities for a client, as a function of its prior catalog access pattern and those of its community peers. The second one constructs dynamic overlays, on behalf of the clients, that are used to share the selected items directly among them. This augments the systems serving capacity and enhances the clients browsing experience. We study the performance of the optimization techniques via numerical experiments. They exhibit efficiency gains over reference methods, by exploiting the content dependencies and correlated community-driven access patterns of the clients. We also report proxy bandwidth savings achieved by our overlay strategy over state-of-the-art methods, on content access patterns of clients with Facebook or Twitter ties.