  In this paper, a video service enhancement strategy is investigated under an
edge-cloud collaboration framework, where video caching and delivery decisions
are made in the cloud and edge respectively. We aim to guarantee the user
fairness in terms of video coding rate under statistical delay constraint and
edge caching capacity constraint. A hybrid human-artificial intelligence
approach is developed to improve the user hit rate for video caching.
Specifically, individual user interest is first characterized by merging
factorization machine (FM) model and multi-layer perceptron (MLP) model, where
both low-order and high-order features can be well learned simultaneously.
Thereafter, a social aware similarity model is constructed to transferred
individual user interest to group interest, based on which, videos can be
selected to cache. Furthermore, a double bisection exploration scheme is
proposed to optimize wireless resource allocation and video coding rate. The
effectiveness of the proposed video caching scheme and video delivery scheme is
finally validated by extensive experiments with a real-world data set.
