In this article, we study the server-side rate adaptation problem for streaming tile-based adaptive 360-degree videos to multiple users who are competing for transmission resources at the network bottleneck. Specifically, we develop a convolutional neural network (CNN)-based viewpoint prediction model to capture the nonlinear relationship between the future and historical viewpoints. A Laplace distribution model is utilized to characterize the probability distribution of the prediction error. Given the predicted viewpoint, we then map the viewport in the spherical space into its corresponding planar projection in the 2-D plane, and further derive the visibility probability of each tile based on the planar projection and the prediction error probability. According to the visibility probability, tiles are classified as viewport, marginal and invisible tiles. The server-side tile rate allocation problem for multiple users is then formulated as a non-linear discrete optimization problem to minimize the overall received video distortion of all users and the quality difference between the viewport and marginal tiles of each user, subject to the transmission capacity constraints and users’ specific viewport requirements. We develop a steepest descent algorithm to solve this non-linear discrete optimization problem, by initializing the feasible starting point in accordance with the optimal solution of its continuous relaxation. Extensive experimental results show that the proposed algorithm can achieve a near-optimal solution, and outperforms the existing rate adaptation schemes for tile-based adaptive 360-video streaming.

中文翻译：

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用于自适应 360 度视频流的基于概率图块可见性的服务器端速率自适应

在本文中，我们研究了将基于 tile 的自适应 360 度视频流式传输到在网络瓶颈处竞争传输资源的多个用户的服务器端速率自适应问题。具体来说，我们开发了一个基于卷积神经网络 (CNN) 的视点预测模型来捕捉未来和历史视点之间的非线性关系。拉普拉斯分布模型用于表征预测误差的概率分布。给定预测的视点，然后我们将球面空间中的视口映射到其在二维平面中对应的平面投影，并进一步根据平面投影和预测误差概率推导出每个瓦片的可见性概率。根据可见概率，瓦片被分类为视口，边缘和隐形瓷砖。然后将多个用户的服务器端瓦片速率分配问题表述为非线性离散优化问题，以最小化所有用户的整体接收视频失真以及每个用户的视口和边缘瓦片之间的质量差异，受传输影响容量限制和用户的特定视口要求。我们开发了一种最速下降算法来解决这个非线性离散优化问题，通过根据其连续松弛的最优解来初始化可行的起点。大量实验结果表明，所提出的算法可以实现接近最优的解决方案，并且优于现有的基于图块的自适应 360 视频流的速率自适应方案。然后将多个用户的服务器端瓦片速率分配问题表述为非线性离散优化问题，以最小化所有用户的整体接收视频失真以及每个用户的视口和边缘瓦片之间的质量差异，受传输影响容量限制和用户的特定视口要求。我们开发了一种最速下降算法来解决这个非线性离散优化问题，通过根据其连续松弛的最优解来初始化可行的起点。大量实验结果表明，所提出的算法可以实现接近最优的解决方案，并且优于现有的基于图块的自适应 360 视频流的速率自适应方案。然后将多个用户的服务器端瓦片速率分配问题表述为非线性离散优化问题，以最小化所有用户的整体接收视频失真以及每个用户的视口和边缘瓦片之间的质量差异，受传输影响容量限制和用户的特定视口要求。我们开发了一种最速下降算法来解决这个非线性离散优化问题，通过根据其连续松弛的最优解来初始化可行的起点。大量实验结果表明，所提出的算法可以实现接近最优的解决方案，并且优于现有的基于图块的自适应 360 视频流的速率自适应方案。