面向移动VR(Virtual Reality)业务的高数据量和低时延需求，为有效降低核心网的数据流量、减小设备的内容请求代价，文中建立了面向移动VR的部分缓存内容共享模型。建模了社交属性感知的内容共享代价最小化问题，将优化问题分解为功率优化与模式选择、设备关联两个子问题，并分别提出基于代价的功率优化和模式选择算法以及基于社交关系和请求代价的匹配算法；最后，通过仿真对提出的基于部分缓存的内容请求模式选择与功率优化的方法的性能进行评价，并与其他缓存方案进行了对比分析。仿真结果表明，文中提出的基于部分缓存的内容请求模式选择与功率优化方案可有效降低内容请求者的请求代价。

Facing the high data volume and low latency requirements of mobile Virtual Reality(VR) services, this paper established a partial cache content sharing model for mobile VR and simulated the social attribute-based content sharing cost minimization problem, in order to effectively reduce the data traffic of the core network and reduce the requesting cost of devices. The problem was decomposed into two sub-problems, namely, power optimization with mode selection and device association. A cost-based power optimization and mode selection algorithm and a matching algorithm based on social relationship and requesting cost were respectively proposed. Finally, the performance of proposed algorithms was evaluated through simulations and was compared with other schemes. The simu-lation results show that the content request mode selection and power optimization scheme based on partial cache can effectively reduce the requesting cost of content requesters.