Two-tier 360° video streaming provides a robust solution for handling inaccurate viewport prediction and varying network conditions. Within this paradigm, the client employs a dual-buffer mechanism consisting of a long buffer for panoramic basic-quality segments and a short buffer for high-quality tiles. However, designing an efficient edge caching strategy for two-tier 360° videos is non-trivial. First, as basic-quality segments and high-quality tiles possess different delivery deadlines as well as content popularity, ignoring these discrepancies may result in inefficient edge caching. Second, accurately predicting the popularity of 360° videos at a fine granularity of video segments and tiles remains a challenge. To address these issues, we present DeCa360, a deadline-aware edge caching framework for 360° videos. Specifically, we introduce a lightweight runtime cache partitioning approach to achieve a careful balance between improving the cache hit ratio and guaranteeing more on-time delivery of objects. Moreover, we design a content popularity prediction method for two-tier 360° videos that combines a learning-based prediction model with domain knowledge of video streaming, leading to improved prediction accuracy and efficient cache replacement. Extensive experimental evaluations demonstrate that DeCa360 outperforms all baseline algorithms in terms of byte-hit ratio and on-time delivery ratio, making it a promising approach for efficient edge caching of 360° videos.

中文翻译：

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DeCa360：用于两层 360° 视频流的截止时间感知边缘缓存


两层 360° 视频流为处理不准确的视区预测和不同的网络条件提供了强大的解决方案。在此范例中，客户端采用双缓冲机制，包括一个用于全景基本质量片段的长缓冲区和一个用于高质量切片的短缓冲区。但是，为两层 360° 视频设计高效的边缘缓存策略并非易事。首先，由于基本质量区段和高质量切片具有不同的交付截止日期和内容受欢迎程度，因此忽略这些差异可能会导致边缘缓存效率低下。其次，在视频片段和图块的精细粒度上准确预测 360° 视频的受欢迎程度仍然是一项挑战。为了解决这些问题，我们提出了 DeCa360，这是一个用于 360° 视频的截止日期感知边缘缓存框架。具体来说，我们引入了一种轻量级运行时缓存分区方法，以在提高缓存命中率和保证更准时地交付对象之间实现谨慎的平衡。此外，我们设计了一种针对两层 360° 视频的内容热度预测方法，该方法将基于学习的预测模型与视频流的领域知识相结合，从而提高了预测准确性和高效的缓存替换。广泛的实验评估表明，DeCa360 在字节命中率和准时送达率方面优于所有基线算法，使其成为 360° 视频高效边缘缓存的一种有前途的方法。