Capture Device Metadata Generation for Media Streaming
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Solution Overview
Problem
Centralized servers face processor-intensive tasks in generating metadata for data from multiple sources, making it difficult to efficiently present and moderate large volumes of content, especially in live streaming scenarios where content identification, grouping, and prioritization are challenging.
Innovation Solution
A system where capture devices generate metadata for their streams, which is then synchronized with the data and transmitted to a server, allowing for dynamic grouping, prioritization, and moderation based on metadata, including location, time, and user-defined characteristics, with features like speech-to-text conversion and watermarking for content management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a central server generates metadata for data from multiple sources, then content can be organized and presented, but the server becomes processor intensive and struggles to handle large volumes of content
Solution Approach 1:
The system divides the metadata generation task between capture devices and the central server. Each capture device generates its own metadata locally, while the server receives and processes only the aggregated metadata from multiple sources. This segmentation reduces the server's processing burden while maintaining the ability to organize and present content effectively.
Solution Approach 2:
Capture devices perform self-service by generating their own metadata independently before transmitting data to the server. This eliminates the need for the server to perform exhaustive processing on all data streams, allowing the server to focus on higher-level tasks such as content aggregation, organization, and presentation.
2Quantity of substance
If a central server receives and processes multiple data streams, then content can be aggregated, but it becomes difficult to identify and present relevant content efficiently
Solution Approach 1:
Metadata is generated and prepared in advance at the capture device before the actual data transmission. This preliminary action includes attaching identifying information, timestamps, and other metadata that will facilitate later content identification and presentation, reducing the server's real-time processing requirements.
Solution Approach 2:
Metadata acts as an intermediary layer between the raw data streams and the final content presentation. The server processes this intermediate metadata layer to create organized content presentations, making it easier to identify and present relevant content from the large volume of aggregated streams.
3Adaptability or versatility
If live content streams are received and processed centrally, then content can be moderated and edited, but the complexity of managing multiple streams increases
Solution Approach 1:
The moderation and editing functions are segmented and assigned to specific modules within the server architecture. Different server modules handle different aspects of moderation and editing for different content streams, reducing the overall complexity of managing multiple streams simultaneously.
Solution Approach 2:
The system dynamically assigns moderation and editing tasks to appropriate server modules based on the content characteristics and stream properties. This dynamic allocation allows the server to manage multiple streams flexibly, adapting the complexity level to the specific requirements of each content stream.
Data Source
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AI summary
There is disclosed a system for providing streaming services, comprising: a plurality of capture devices, each for capturing data and providing a captured data stream; and a server, for receiving the plurality of captured data streams; wherein each capture device is configured to generate metadata for the captured data, and transmit said metadata to the server.