PARIMA: Viewport Adaptive 360-Degree Video Streaming
This addresses the need for optimized streaming algorithms for 360-degree videos, which is a domain-specific problem for video streaming applications, and is incremental as it builds on existing methods by incorporating video content and dynamic adaptation.
The paper tackles the problem of high bandwidth requirements for streaming 360-degree videos by developing PARIMA, an online viewport prediction model that uses past viewports and object trajectories to predict future viewports, resulting in a 30% improvement in Quality of Experience while maintaining short response time.
With increasing advancements in technologies for capturing 360° videos, advances in streaming such videos have become a popular research topic. However, streaming 360° 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 predict future viewports. We claim that the head movement of a user majorly depends upon the trajectories of the prime objects in the video. We employ a pyramid-based bitrate allocation scheme and perform a comprehensive evaluation of the performance of PARIMA. In our evaluation, we show that PARIMA outperforms state-of-the-art approaches, improving the Quality of Experience by over 30\% while maintaining a short response time.