Multiview Video Window Generation Using Preliminary Buffering
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Solution Overview
Problem
Current digital media processing systems lack the ability to efficiently manage and enhance user experiences by providing concurrent video streams and dynamically inserting relevant content based on user interest, often requiring significant computational resources and user interaction.
Innovation Solution
A digital media system that generates and manages concurrent video streams by detecting user interactions and trigger events, using machine learning and artificial intelligence to insert relevant content into secondary streams, thereby improving user engagement and reducing computational burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system provides multiple concurrent video streams to users, then user experience and engagement are improved, but computational resources and processing power are significantly consumed
Solution Approach 1:
The system segments video content into multiple independent streams that can be processed and delivered separately. Each video stream is treated as an independent unit that can be generated, buffered, and transmitted concurrently, allowing the system to provide multiple views without proportionally increasing processing complexity for each individual stream.
Solution Approach 2:
The system performs preliminary actions by pre-generating and buffering multiple video streams before they are needed for delivery. Video content is processed and prepared in advance, stored in buffers, and made ready for concurrent transmission, which eliminates the need for real-time processing of multiple streams and significantly reduces computational resource requirements during actual delivery.
2Productivity
If the system dynamically inserts relevant content into video streams based on user interest, then user engagement is improved, but the complexity of content management and processing increases
Solution Approach 1:
The system implements self-service by using automated machine learning models and algorithms to detect user interest and dynamically insert relevant content into video streams without requiring manual intervention. The content management system automatically analyzes user behavior, identifies relevant content, and performs insertions, eliminating the need for extensive administrative resources and reducing operational complexity.
Solution Approach 2:
The system changes parameters by dynamically adjusting content insertion based on detected user interest levels and patterns. The machine learning models analyze user engagement metrics and modify content delivery parameters in real-time, such as selecting which ads or content to insert, when to insert them, and at what positions, thereby improving user engagement while maintaining manageable system complexity through automated decision-making.
3Loss of energy
If the system reduces computational resources required for streaming, then operational costs are reduced, but the ability to provide enhanced user experiences may be limited
Solution Approach 1:
The system performs preliminary processing and buffering of video streams in advance, storing multiple concurrent streams in memory or storage buffers before delivery. This pre-computation approach allows the system to reduce computational resource requirements during actual streaming operations while still providing enhanced user experiences through multiple concurrent video streams and dynamic content insertion.
Solution Approach 2:
The system creates and manages multiple copies of video content in different stream formats simultaneously. By pre-generating and buffering these copies, the system can deliver multiple concurrent streams without proportionally increasing processing power requirements, as the heavy computational work of video encoding and processing is performed in advance rather than in real-time during stream delivery.
Data Source
AI summary
A digital media system is configured to provide modified concurrent video streams to a client device. A plurality of concurrent video streams is accessed. The plurality of concurrent video streams includes a first concurrent video stream. It is detected that the first concurrent video stream is selected for display in a primary window among a set of windows to be displayed on a display screen. A second concurrent video stream is generated based on the first concurrent video stream. The plurality of concurrent video streams is modified by adding the generated second concurrent video stream to the plurality of concurrent video streams. The modified plurality of concurrent video streams is provided to a device configured to display the modified plurality of concurrent video streams concurrently together in corresponding different windows among the set of windows.