Digital Media System for Dynamic VR Content Adaptation
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Solution Overview
Problem
Current digital media processing systems lack efficient methods for providing immersive virtual-reality content with directional audio and picture-in-picture windows, dynamic adjustments based on network capacity, and personalized user recommendations, leading to suboptimal user experiences and resource utilization.
Innovation Solution
A digital media system that dynamically adjusts video file lengths and numbers based on network capacity, supports directional audio and picture-in-picture windows aligned with user viewing directions, and uses machine learning for personalized content recommendations, all while optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If dynamic adjustment of video file lengths and numbers based on network capacity is implemented, then resource utilization is optimized, but system complexity increases
Solution Approach 1:
The system dynamically adjusts video file lengths and quantities based on real-time network capacity measurements. The server modifies streaming parameters adaptively, changing from static to dynamic resource allocation to optimize resource utilization while managing system complexity through automated control mechanisms.
Solution Approach 2:
The system changes streaming parameters including video file length and number of files based on network capacity. By modifying these parameters dynamically, the system optimizes resource utilization without requiring complex manual intervention, allowing the system to self-adjust to network conditions.
2Ease of operation
If directional audio and picture-in-picture windows aligned with viewing direction are provided, then user experience is enhanced, but device complexity increases
Solution Approach 1:
The system provides directional audio and picture-in-picture windows specifically aligned with the user's viewing direction. Instead of uniform omnidirectional content, the system delivers localized enhanced quality content in the direction of interest, improving user experience by making the audio and visual elements spatially relevant to the user's perspective.
3Productivity
If machine learning for personalized content recommendations is implemented, then user engagement is improved, but device complexity increases
Solution Approach 1:
The system uses machine learning to automatically generate personalized content recommendations without requiring manual user input or configuration. The system serves itself by learning user preferences and automatically adjusting content delivery to maximize engagement, reducing the need for complex user interaction while improving productivity.
Data Source
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AI summary
A digital media system is configured to support any one or more of multiple features with respect to virtual-reality content. Examples of such features include directional picture-in-picture (PIP) windows based on viewing direction, directional audio based on viewing direction, user recommendations based on anomalous viewing times of certain visual features in video content, dynamic adjustment of time-to-live (TTL) durations prior to requesting deletion of video files uploaded to a content distribution network, dynamic adjustment of durations of video files to upload based on network capacity, dynamic adjustment of quantities of video files per set to upload based on network capacity, dynamic resizing of top-depicting or bottom-depicting regions within the picture areas of sets of video files, dynamic resizing of the picture areas themselves within sets of video files, or any suitable combination thereof.