This paper considers encoding caching and collaborative distribution issues jointly, and proposes a collaborative and efficient content caching and distribution mechanism; and based on the proposed scheme, an energy consumption model is established from the perspective of content caching and content distribution to optimize Energy efficiency of content caching and content distribution. In addition, this paper proposes a cache placement algorithm based on heuristic greedy algorithm to solve this energy efficiency optimization problem by optimizing cache placement. Finally, the simulation results show the performance of the scheme. This paper proposes a content clustering distribution scheme for the problems of random user distribution and limited cache. This solution uses the user’s media cloud and communication technology to complete the distribution of video content. In addition, the use of an efficient adaptive media cloud clustering mechanism and a minimized hops algorithm has enabled the reasonable deployment of 5G network caches based on the popularity of video content and user distribution. The simulation results show that the system content request response time is shortened by 50%, which effectively reduce the impact of content popularity changes on system performance. This paper analyses the important performance indicators such as cache hit rate, optimal communication radius, and maximum link access in 5G networks for multi-layer popular multimedia content distribution in response to the heterogeneity and transmission interference problems for 5G networks in 5G communications. Under the premise of noise ratio and signal-to-interference ratio threshold, related service link selection and scheduling algorithms is further proposed obtaining simulation results that approximate theoretical analysis, which effectively reduces the number of interference by 40% and improves the system throughput by 10%.

本文考虑了编码缓存和协作分发问题，并提出了一种协作高效的内容缓存和分发机制。在此方案的基础上，从内容缓存和内容分发的角度建立了能耗模型，以优化内容缓存和内容分发的能源效率。此外，本文提出了一种基于启发式贪婪算法的缓存放置算法，以通过优化缓存放置来解决能源效率优化问题。最后，仿真结果表明了该方案的性能。针对用户随机分配和缓存受限的问题，提出了一种内容聚类分配方案。该解决方案使用用户的媒体云和通信技术来完成视频内容的分发。此外，基于视频内容的普及和用户分布情况，高效的自适应媒体云集群机制和最小化的跳数算法的使用已使5G网络缓存的合理部署成为可能。仿真结果表明，系统内容请求响应时间缩短了50％，有效降低了内容流行度变化对系统性能的影响。本文针对5G通信中5G网络的异构性和传输干扰问题，分析了5G网络中用于多层流行多媒体内容分发的重要性能指标，例如缓存命中率，最佳通信半径和最大链路访问量。