Constrained Manifest Data for Playback System Characterization
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Solution Overview
Problem
Existing media content delivery systems are limited in their ability to accurately account for the playback capabilities of diverse content playback systems, leading to mismatches in media quality and potential deterioration in playback experience due to heuristic algorithms solely relying on client device capabilities.
Innovation Solution
A deep characterization process that collects and analyzes parameters from various components of a content playback system, including static and dynamic factors, to generate constrained manifest data that aligns media quality options with the overall system capabilities, allowing the server to influence playback decisions and prevent mismatches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If heuristic algorithms for requesting fragments are implemented on the client device, then the client device can autonomously adjust media quality based on bandwidth and hardware resources, but the media server is limited in its ability to influence the playback of the media content
Solution Approach 1:
The patent introduces constrained manifest data as an intermediary mechanism that bridges the server and client heuristic algorithms. The server generates this constrained manifest data that encodes its preferences and capabilities, which then guides the client-side heuristic algorithm's fragment selection process. This allows the server to influence playback decisions without directly controlling the client's autonomous decision-making process.
2Adaptability or versatility
If adaptive bitrate streaming adjusts media quality based on client device capabilities, then playback can adapt to bandwidth and hardware changes, but mismatches in media quality and playback experience can occur due to insufficient characterization of the entire playback system
Solution Approach 1:
The patent extends the characterization from a single client device dimension to multiple dimensions by incorporating parameters from the entire playback system including external displays, audio devices, and other connected components. This multi-dimensional characterization approach enables more accurate matching of media quality to the complete playback chain rather than just the client device.
Solution Approach 2:
The system performs preliminary characterization of the entire playback system before media delivery begins. By collecting and analyzing parameters from all playback components in advance, the server can pre-generate constrained manifest data that anticipates the system's capabilities and limitations, preventing quality mismatches before they occur during playback.
3Reliability
If the server generates constrained manifest data based on deep characterization of the playback system, then the server can influence playback decisions and prevent quality mismatches, but the system complexity increases due to parameter collection and analysis
Solution Approach 1:
The patent segments the complex characterization process into distinct functional modules: parameter collection from various playback components, parameter analysis and processing, and constrained manifest data generation. This segmentation allows each module to be developed and optimized independently, managing the overall system complexity while maintaining reliable playback quality matching.
Data Source
AI summary
Techniques are described for obtaining and analyzing deep characterization information for a content playback system with multiple interconnected components. A server can determine a subset of configurations of requested media content that are compatible with the capabilities of the components of the content playback system. Limiting a client device to select media content fragments corresponding to the subset of configurations can be enforced through the use of constrained manifest files.