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.
翻译:在本文中,对视频服务强化战略进行了边际协作框架调查,分别在云层和边缘进行视频缓存和交付决定。我们的目标是在统计延迟限制和边端缓存能力限制下,确保视频编码率的用户公平;开发了混合人类人工智能方法,以提高视频缓存的用户点击率。具体地说,单个用户的兴趣首先体现在集成因子化机(FM)模型和多层透视器(MLP)模型上,可以同时很好地学习低级和高级特征。随后,构建了一个社会认知相似性模型,将个人用户兴趣转移给群体兴趣,在此基础上,可以选择视频作为缓存。此外,还提议了一个双分解勘探计划,优化无线资源分配和视频编码率。拟议的视频缓存机制和视频传输计划的有效性最终通过与真实世界数据集的广泛实验得到验证。