Grid Encoding for Media Asset Data Fragmentation
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Solution Overview
Problem
Existing communication networks face inefficiencies in delivering media content due to the need for multiple data blobs to accommodate different combinations of video and audio CODECs, leading to an unwieldy system as the number of encoding types increases, requiring significant storage and transmission resources.
Innovation Solution
The implementation of grid encoding, where media asset data is organized in a grid format with one axis representing time segments and the other axis representing different data types, allowing for efficient access and transmission of data compatible with specific devices, eliminating the need for multiple blob encodings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data blobs are created for each combination of video and audio CODECs, then compatibility with different end devices is improved, but system complexity and storage requirements increase significantly
Solution Approach 1:
The patent segments media asset data into discrete, independently decodable units called fragments. Each fragment contains a complete representation of a portion of the asset (video, audio, or enhancement data) encoded with a specific CODEC. This segmentation allows the system to provide compatibility for multiple CODECs without creating complete duplicate blobs, as fragments can be selectively assembled based on device capabilities.
Solution Approach 2:
The patent implements local quality by allowing different fragments to have different encoding characteristics optimized for specific device requirements. Each fragment is tagged with metadata indicating its CODEC type, temporal segment, and data type, enabling the system to provide locally optimized quality for each device while maintaining overall system efficiency.
2Adaptability or versatility
If multiple data blobs are stored for different encoding combinations, then all device types can be served, but storage space and transmission bandwidth are wasted
Solution Approach 1:
The patent creates a universal fragment structure that can serve multiple functions across different device types. Each fragment is designed to be independently useful and can be combined with other fragments to serve various CODEC requirements. This multi-functional design eliminates the need to store separate complete blobs for each device type, as the same fragment can be reused across multiple playback scenarios.
Solution Approach 2:
The patent enables selective discarding of fragments that are not needed for a particular device playback scenario. The system recovers and transmits only the specific fragments required for the requesting device's capabilities, rather than transmitting entire data blobs containing all possible encodings. This selective approach significantly reduces storage and transmission resource consumption.
3Adaptability or versatility
If conventional blob encoding is used to provide all CODEC combinations, then complete compatibility is achieved, but the approach becomes unwieldy as encoding types increase
Solution Approach 1:
The patent introduces a new organizational dimension by tagging fragments with metadata that identifies their temporal segment, data type, and CODEC characteristics. This dimensional tagging system transforms the management of multiple encodings from a combinatorial explosion problem into a structured, queryable format where fragments can be efficiently selected and assembled based on device requirements without manual management of complete blobs.
Data Source
AI summary
Metadata can identify temporal segments of content associated with a media asset and can identify asset data types. In response to a request for a particular asset, a central office may identify a data type and a temporal segment based on metadata associated with the request.