Immersive Video Streaming View-Adaptive Prefetching
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Solution Overview
Problem
Current technologies for streaming immersive video content face challenges in efficiently managing computational and network resources, particularly in predicting user viewing perspectives and adapting data transmission based on user behavior and network conditions, leading to potential interruptions and reduced quality.
Innovation Solution
A method involving the use of multiple levels of detail for immersive video content, where initial data is stored in a video buffer, and additional data is selectively streamed based on predicted user viewports, using different communications links to optimize resource usage and ensure uninterrupted streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple levels of detail data are stored in video buffer, then video quality is improved, but device complexity and memory usage increase
Solution Approach 1:
The video content is segmented into multiple levels of detail (LOD), with a base layer stored in the video buffer and enhancement layers selectively streamed. This segmentation allows the system to maintain a manageable buffer size while providing the capability to deliver high-quality video when needed, resolving the contradiction between video quality and device complexity.
Solution Approach 2:
The base layer of video data is preliminarily stored in the video buffer before playback, ensuring that a minimum quality level is always available. Enhancement layers are prepared and streamed on-demand based on predicted user viewports, allowing the system to maintain video quality without requiring all high-detail data to be pre-stored, thus reducing device complexity.
2Manufacturing precision
If high-resolution video data is streamed, then video quality is improved, but network bandwidth consumption increases
Solution Approach 1:
Instead of uniformly streaming high-resolution data across the entire video content, the system applies local quality enhancement only to the predicted viewport regions where the user is actually looking. This allows high video quality to be delivered in the regions of interest while using minimal network bandwidth for the rest of the content.
Solution Approach 2:
The system streams only the necessary portion of enhancement layer data corresponding to the predicted viewport rather than the entire high-resolution content. This partial action approach delivers sufficient video quality for the user's actual viewing area while significantly reducing network bandwidth consumption compared to streaming complete high-resolution video.
3Adaptability or versatility
If viewport prediction is performed, then adaptability is improved, but computational requirements increase
Solution Approach 1:
Viewport prediction is performed preliminarily before the actual video playback to anticipate the user's viewing direction. This allows the system to pre-fetch and buffer the necessary enhancement layer data in advance, reducing the need for complex real-time computational adjustments during playback and thereby lowering overall computational requirements.
Solution Approach 2:
The system uses feedback from user behavior data and metadata to continuously refine viewport predictions. This feedback mechanism improves adaptability by making predictions more accurate over time, while also reducing computational requirements by focusing processing on the most likely viewport regions rather than computing all possible view directions.
4Productivity
If selective data streaming is performed, then resource efficiency is improved, but reliability decreases
Solution Approach 1:
The system applies beforehand cushioning by pre-buffering the base layer video data and using viewport prediction to anticipate future viewing needs. This creates a buffer of pre-fetched enhancement layer data that cushions against network variability, ensuring that even if selective streaming encounters interruptions, the user experience remains reliable due to the head-start buffer of predicted viewport data.
Data Source
AI summary
A system obtains a data set representing immersive video content for display at a display time, including first data representing the content according to a first level of detail, and second data representing the content according to a second higher level of detail. During one or more first times prior to the display time, the system causes at least a portion of the first data to be stored in a buffer. During one or more second times prior to the display time, the system generates a prediction of a viewport for displaying the content to a user at the display time, identifies a portion of the second data corresponding to the prediction of the viewport, and causes the identified portion of the second data to be stored in the video buffer. At the display time, the system causes the content to be displayed to the user using the video buffer.