Edge-FVV: Free Viewpoint Video Streaming by Learning at the Edge
Edge-FVV: Free Viewpoint Video Streaming by Learning at the Edge
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DOI：
10.1109/icme55011.2023.00344
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Hanwang Zhang;Jie Zhang;Weimiao Feng;Kaigui Bian;Hu Tuo
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文献类型：
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作者：
Hanwang Zhang;Jie Zhang;Weimiao Feng;Kaigui Bian;Hu Tuo
观众可以从多个角度（即视点）观看视频，获得身临其境的体验。免费视点视频（FVV）的开发，使用户能够选择自己喜欢的视点在播放视频。但是，如果所选视点的视频帧不能及时加载，或者从相邻视点的多个视频流合成，用户可能会遇到延迟。为了解决这个问题，我们提出了edge- FVV，一种边缘辅助FVV系统，它使用边缘缓存来减少从服务器到客户端用户的请求FVV流的延迟。我们首先分析了响应FVV请求时边缘缓存的容量和延迟。接下来，我们提出了两种类型的机器学习算法，将用户的请求分配到适当的边缘缓存。我们的评估表明，在减少FVV请求的延迟方面，两种提出的算法分别比基准测试高出4.2-7.4%和4.6-6.8%。
Audiences cangain an immersive experience watching videos from multiple angles (a.k.a. viewpoints). Free Viewpoint Video (FVV) is developed to enable users to choose their preferred viewpoints during the play of a video. However, users may experience a delay if video frames of the chosen viewpoint cannot be timely loaded, or synthesized from multiple video streams of neighboring viewpoints. To address this problem, we present Edge-FVV, an edge-assisted FVV system that employs edge caches to reduce the delay in streaming the requested FVV from the server to client users. We first analyze the capacity and delay at edge caches when answering FVV requests. Next, we propose two types of machine learning algorithms that allocate the users’ requests to appropriate edge caches. Our evaluation shows that two types of proposed algorithms outperform benchmarks by 4.2-7.4% and 4.6-6.8%, respectively, in reducing the delay for FVV requests.