论文标题
360美元
Viewport-Aware Deep Reinforcement Learning Approach for 360$^o$ Video Caching
论文作者
论文摘要
360 $^o $视频是VR/AR/MR系统的重要组成部分，可为用户提供沉浸式体验。但是，360 $^o $视频与高带宽要求有关。可以通过利用用户有兴趣仅查看视频场景的一部分，并且用户请求彼此重叠的视口的事实可以减少所需的带宽。在最近的作品的发现中，在Edge服务器中缓存视频瓷砖的好处而不是缓存整个360 $^o $视频的益处，在本文中，我们介绍了具有与原始视口相同数量的虚拟视口的概念。形成这些视口的瓷砖是每个视频最受欢迎的瓷砖，并由用户的请求决定。然后，我们提出了一个积极主动的缓存计划，该计划假定未知视频和视口的受欢迎程度。我们的方案确定了要缓存的视频以及每个视频的最佳虚拟视口。虚拟视口允许降低缓存优化问题的维度。为了解决问题，我们首先将360 $^o $ $视频的内容放置在边缘缓存网络中作为马尔可夫决策过程（MDP），然后使用深Q-Network（DQN）算法确定最佳的缓存位置。拟议的解决方案旨在通过在基本质量的最受欢迎的360 $^o $视频中加速最终用户的360美元$^o $视频的整体质量以及高质量的虚拟视口。我们广泛评估了所提出的系统的性能，并将其与LFU，LRU，FIFO等已知系统的性能进行了比较，而不是合成和真实的360 $^o $视频痕迹。结果表明，从渲染视口的整体质量，高速公路命中率和维修成本方面，虚拟视口积极缓存而不是原始的福利带来的巨大好处。
360$^o$ video is an essential component of VR/AR/MR systems that provides immersive experience to the users. However, 360$^o$ video is associated with high bandwidth requirements. The required bandwidth can be reduced by exploiting the fact that users are interested in viewing only a part of the video scene and that users request viewports that overlap with each other. Motivated by the findings of recent works where the benefits of caching video tiles at edge servers instead of caching entire 360$^o$ videos were shown, in this paper, we introduce the concept of virtual viewports that have the same number of tiles with the original viewports. The tiles forming these viewports are the most popular ones for each video and are determined by the users' requests. Then, we propose a proactive caching scheme that assumes unknown videos' and viewports' popularity. Our scheme determines which videos to cache as well as which is the optimal virtual viewport per video. Virtual viewports permit to lower the dimensionality of the cache optimization problem. To solve the problem, we first formulate the content placement of 360$^o$ videos in edge cache networks as a Markov Decision Process (MDP), and then we determine the optimal caching placement using the Deep Q-Network (DQN) algorithm. The proposed solution aims at maximizing the overall quality of the 360$^o$ videos delivered to the end-users by caching the most popular 360$^o$ videos at base quality along with a virtual viewport in high quality. We extensively evaluate the performance of the proposed system and compare it with that of known systems such as LFU, LRU, FIFO, over both synthetic and real 360$^o$ video traces. The results reveal the large benefits coming from proactive caching of virtual viewports instead of the original ones in terms of the overall quality of the rendered viewports, the cache hit ratio, and the servicing cost.
