支持MEC的小蜂窝网络中用于VR交付的水平和垂直协作
Horizontal and Vertical Collaboration for VR Delivery in MEC-Enabled Small-Cell Networks
摘要：
由于虚拟现实（VR）视频应用具有大带宽、低延迟和计算密集型的特点，目前资源受限的无线和边缘网络无法满足按需VR交付的要求。在这封信中，我们提出了一个联合的水平和垂直协作架构，用于支持移动边缘计算（MEC）的小型蜂窝网络，以实现VR交付。在所提出的架构中，多个MEC服务器可以联合为VR头戴式设备（HMD）提供边缘缓存和视点计算服务，而计算任务也可以在HMD或云上执行。基站（BS）处的功率分配被认为与MEC服务器的水平协作（HC）和垂直协作（VC）相协调，以获得VR递送的较低的端到端延迟。提出了一个联合缓存、功率分配和任务卸载问题，并提出了一种基于单调优化的离散分支归约定界（DBRB）算法来有效地解决该问题.仿真结果表明，所提出的架构和算法的优势，在现有的。
英文摘要：
Due to the large bandwidth, low latency and computationally intensive features of virtual reality (VR) video applications, the current resource-constrained wireless and edge networks cannot meet the requirements of on-demand VR delivery. In this letter, we propose a joint horizontal and vertical collaboration architecture in mobile edge computing (MEC)-enabled small-cell networks for VR delivery. In the proposed architecture, multiple MEC servers can jointly provide VR head-mounted devices (HMDs) with edge caching and viewpoint computation services, while the computation tasks can also be performed at HMDs or on the cloud. Power allocation at base stations (BSs) is considered in coordination with horizontal collaboration (HC) and vertical collaboration (VC) of MEC servers to obtain lower end-to-end latency of VR delivery. A joint caching, power allocation and task offloading problem is then formulated, and a discrete branch-reduce-and-bound (DBRB) algorithm inspired by monotone optimization is proposed to effectively solve the problem. Simulation results demonstrate the advantage of the proposed architecture and algorithm in terms of existing ones.
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