Remote Encoding Metadata for Local Content Devices
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Solution Overview
Problem
Existing content encoding mechanisms require significant memory and processing power, making it difficult for smaller local content receivers, such as mobile devices, to efficiently encode audiovisual content, as they need to analyze and determine the best encoding method.
Innovation Solution
Generating encoding metadata at a content distributor and providing it to a local content-receiver-and-distribution device, allowing the device to encode content without analyzing it, thus saving processing resources and reducing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If local content receivers perform content encoding analysis to determine encoding methods, then encoding quality can be optimized, but processing power and memory requirements increase significantly
Solution Approach 1:
The content distributor performs content encoding analysis and determines optimal encoding methods in advance, before the content is received by the local device. The distributor generates encoding metadata that includes encoding parameters and instructions, which are then transmitted to the local content receiver. This preliminary action eliminates the need for the local device to perform complex encoding analysis, thereby maintaining high encoding quality while significantly reducing processing power requirements at the local device.
Solution Approach 2:
The invention introduces encoding metadata as an intermediary element that carries encoding instructions from the content distributor to the local content receiver. This metadata acts as a mediator that enables the local device to perform encoding without having the analytical capabilities to determine optimal encoding methods itself. The encoding metadata includes parameters such as encoding format, compression levels, and other settings that guide the local device's encoding process, thus resolving the contradiction between encoding quality and processing requirements.
2Adaptability or versatility
If content encoding analysis is performed at local devices, then encoding adaptability to different content types is improved, but computational costs and resource consumption increase
Solution Approach 1:
The content distributor performs content analysis and determines optimal encoding parameters in advance for different content types. The distributor creates a library of encoding metadata templates that can be applied to various content types without requiring real-time analysis at the local device. When content is received, the local device simply retrieves the appropriate pre-computed encoding metadata, enabling adaptability to different content types while minimizing computational costs at the local device.
Solution Approach 2:
The system uses encoding metadata that contains encoded parameter information for different content types and scenarios. The metadata includes parameters such as encoding format (e.g., H.264, H.265), resolution, frame rate, compression level, and other encoding settings that are optimized for specific content types. By changing these parameters based on the content type without requiring local analysis, the system achieves encoding adaptability while keeping computational requirements low at the local device.
3Device complexity
If local content receivers encode content without receiving encoding metadata, then device simplicity is maintained, but encoding efficiency and quality deteriorate
Solution Approach 1:
The encoding metadata serves as an intermediary that bridges the gap between device simplicity and encoding efficiency. The metadata contains comprehensive encoding instructions and parameters that guide the local device's encoding process. By receiving and applying this metadata, the simple local device can achieve encoding efficiency comparable to complex centralized encoding systems. The metadata acts as a knowledge transfer mechanism that enables efficient encoding without requiring complex local processing capabilities.
Solution Approach 2:
Instead of requiring the local device to independently determine optimal encoding methods through complex analysis, the system copies the encoding metadata from the content distributor to the local device. This metadata copy includes all necessary encoding parameters, instructions, and optimizations that were determined by the distributor's analysis. By copying and applying this metadata, the local device achieves high encoding efficiency without having to perform the complex analysis itself, thus maintaining device simplicity while improving encoding productivity.
Data Source
AI summary
Embodiments are directed towards remotely generating encoding metadata at a remote content distributor for use by a local user computing device. The remote content distributor receives and encodes content. During or after the encoding process, the remote content distributor generates encoding metadata that indicates how the content was encoded by the remote content distributor. The remote content distributor provides the encoding metadata to the user computer device. The user computing device receives the content and the encoding metadata and encodes the content based on the encoding metadata. The user computing device can then provide the encoded content to another computing device for decoding and presentation to a user.