OpTile: Toward Optimal Tiling in 360-degree Video Streaming
OpTile: Toward Optimal Tiling in 360-degree Video Streaming
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DOI：
10.1145/3123266.3123339
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Mengbai Xiao;Chao Zhou;Yao Liu;Songqing Chen
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文献类型：
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作者：
Mengbai Xiao;Chao Zhou;Yao Liu;Songqing Chen
360-通过首先将球形表面投影到二维帧上，然后将这些帧编码为标准视频段，来对30度视频进行编码以用于自适应流传输。在这些360度视频的回放期间，视频播放器在用户的观看方向上渲染球形表面的部分。这些用户视口通常仅覆盖360度表面的一小部分，导致大量下载的带宽被浪费。基于瓦片的方法可以通过将视频空间切割成运动受限的矩形来减少浪费的带宽。流逻辑然后仅需要下载渲染用户看到的视口所需的图块。现有的基于图块的方法将360度视频切割成固定大小的图块。然而，这些固定大小的平铺方法遭受降低的编码效率。分块将视频中可以由编码器从相邻帧或当前帧中复制的部分切掉，这些部分是有效视频压缩所需的。在本文中，我们提出了一个方案称为OpTile。该方案通过首先估计每瓦片存储成本，然后求解整数线性规划（ILP）以获得最佳的、可能不均匀的瓦片，来瓦片投影的360度段。ILP目标考虑了特定于内容的特征和用户对细分的看法的经验分布。使用随机选择的训练/测试集分裂，我们表明，如果流媒体算法可以完美地预测用户的头部方向，我们提出的方案可以节省高达73%的下载数据相比，非平铺方案和高达44%相比，性能最好的均匀平铺方法。
360-degree videos are encoded for adaptive streaming by first projecting the spherical surface onto two-dimensional frames, then encoding these as standard video segments. During playback of these 360-degree videos, the video player renders the portion of the spherical surface in the direction of the user's view. These user viewports typically cover only a small portion of the 360 degree surface, causing much of the downloaded bandwidth to be wasted. Tile-based approaches can reduce the wasted bandwidth by cutting video spatially into motion-constrained rectangles. Streaming logic then only needs to download the tiles necessary to render the viewport seen by the user. Existing tile-based approaches cut 360-degree videos into tiles of fixed sizes. These fixed-size tiling approaches, however, suffer from reduced encoding efficiency. Tiling cuts away portions of the video that can be copied by the encoder from adjacent frames or within the current frame that are needed for effective video compression. In this paper, we propose a scheme called OpTile. This scheme tiles a projected 360-degree segment by first estimating per-tile storage costs, then solving an integer linear program (ILP) to obtain an optimal, potentially non-uniform tiling. The ILP objective considers both content-specific characteristics and empirical distributions over user views of the segments. Using a randomly selected training/testing set split, we show that if a streaming algorithm can perfectly predict the user head orientation, our proposed scheme can save up to 73% of downloaded data compared to the non-tiling scheme and up to 44% compared to the best-performing uniform tiling methods.