TL;DR
PARIMA is an online viewport prediction model for 360-degree video streaming that leverages user head movement and prime object trajectories to optimize bitrate allocation, significantly enhancing user experience.
Contribution
It introduces a novel, content-aware, real-time viewport prediction method that adapts to user preferences and video content, outperforming existing approaches.
Findings
Improves Quality of Experience by over 30%
Outperforms state-of-the-art viewport prediction methods
Maintains short response time for real-time streaming
Abstract
With increasing advancements in technologies for capturing 360{\deg} videos, advances in streaming such videos have become a popular research topic. However, streaming 360{\deg} videos require high bandwidth, thus escalating the need for developing optimized streaming algorithms. Researchers have proposed various methods to tackle the problem, considering the network bandwidth or attempt to predict future viewports in advance. However, most of the existing works either (1) do not consider video contents to predict user viewport, or (2) do not adapt to user preferences dynamically, or (3) require a lot of training data for new videos, thus making them potentially unfit for video streaming purposes. We develop PARIMA, a fast and efficient online viewport prediction model that uses past viewports of users along with the trajectories of prime objects as a representative of video content to…
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