Asynchronous Media Fabric for Real-Time Granular Stream Distribution
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Solution Overview
Problem
Existing protocols like SMPTE 2022-6 and SMPTE 2110 are not suitable for asynchronous media processing in cloud or data center environments, imposing tight time constraints and limiting the efficient use of computing resources.
Innovation Solution
The Asynchronous Media Fabric (AMF) framework allows media processing functions to operate asynchronously, using shared memory pools and API endpoints for efficient data transfer and control, while respecting real-time constraints at input/output ports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If SMPTE 2022-6 and SMPTE 2110 protocols are used for real-time media transport over ethernet, then real-time transmission with tight time constraints is achieved, but the system becomes unsuitable for asynchronous media processing in cloud or data center environments
Solution Approach 1:
The system segments media processing into independent asynchronous functions that can be executed on multiple hosts. Each function processes media grains independently, allowing flexible distribution across the network without rigid time constraints, thus resolving the contradiction between real-time speed and asynchronous adaptability
Solution Approach 2:
The system dynamically adjusts media processing timing based on availability rather than enforcing fixed time constraints. Functions are invoked when data is ready and results are consumed when available, creating a dynamic adaptive system that works both in real-time and cloud environments
2Productivity
If multiple hosts process the same media stream asynchronously, then computing resource efficiency is improved, but coordinating real-time constraints and data synchronization becomes more complex
Solution Approach 1:
The system introduces an intermediary coordination layer that manages data exchange between multiple asynchronous hosts. This mediator handles synchronization and real-time constraint coordination automatically, allowing multiple hosts to process media efficiently without increasing operational complexity
Solution Approach 2:
The system adds a temporal dimension to media processing by allowing functions to execute at different times without losing synchronization. This dimensional approach enables asynchronous processing while maintaining real-time constraints through time-aware data management
3Reliability
If tight time constraints are imposed on ethernet packet transmission to simulate SDI timing, then real-time transmission quality is maintained, but the system cannot efficiently utilize asynchronous computing capabilities
Solution Approach 1:
The system performs preliminary actions by pre-processing media data and preparing functions in advance, then executes them asynchronously when resources are available. This allows real-time quality to be maintained through proper timing of critical operations while maximizing asynchronous computing utilization through batch processing and background tasks
Data Source
AI summary
A system and method for an asynchronous media fabric service. The system includes at least one host configured with the asynchronous media fabric service. The asynchronous media fabric service is configured to: create and delete shared memory pools that are used to allow processes running on a single host direct access to grain data; send and receive grains from other hosts; and serve as an API endpoint for asynchronous media fabric function processes to create, delete and obtain references to streams.