Live Stream Analytics Engine for Network Path Optimization
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Solution Overview
Problem
Live and VOD stream quality often suffers due to unfavorable network conditions, and publishers lack the knowledge or tools to identify and switch to better network paths or adjust parameters in real-time, leading to frustrating viewing experiences.
Innovation Solution
Implementing a system that tests live streams before going live using a test audience based on historical data, collects analytics to determine favorable network paths and parameters, and makes on-the-fly adjustments during streaming to prevent issues, while also using post-stream analytics to improve future streaming quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time network path switching and parameter adjustment are implemented to improve stream quality, then viewing conditions are improved, but system complexity and response time requirements increase
Solution Approach 1:
The patent introduces an intermediary system (analytics engine and control system) that mediates between the network conditions and the streaming process. This intermediary automatically monitors network quality, identifies favorable paths, and adjusts streaming parameters without requiring the publisher to manually intervene, thus improving stream quality while managing system complexity through automation.
Solution Approach 2:
The system implements continuous feedback loops where analytics data from network monitoring is fed back to the control system, which then adjusts streaming parameters in real-time. This closed-loop feedback mechanism enables automatic adaptation to changing network conditions, improving reliability while reducing the need for complex manual control systems.
2Reliability
If manual network parameter adjustment is performed to improve viewing conditions, then stream quality can be optimized, but response time is delayed and publisher expertise is required
Solution Approach 1:
The system enables self-service automation where the streaming system automatically monitors its own performance, identifies network issues, and adjusts parameters without external intervention. The analytics engine continuously evaluates network conditions and the control system autonomously implements optimizations, eliminating the time delay associated with manual publisher intervention and removing the need for specialized expertise.
Solution Approach 2:
The system performs preliminary actions by pre-identifying favorable network paths and pre-configuring optimal streaming parameters before quality degradation occurs. The continuous monitoring and predictive analytics allow the system to proactively switch to better network paths before viewers experience quality issues, reducing response time and preventing problems rather than reacting to them.
3Reliability
If test streaming to historical audience is performed before going live, then stream configuration issues can be detected, but additional time and resources are required
Solution Approach 1:
The system performs preliminary testing actions by streaming test content to a subset of historical audience members before the full live broadcast. This advance testing identifies configuration issues, network problems, and compatibility concerns in advance of the main event, allowing time for corrections while still maintaining a compressed timeline through automated testing procedures.
Solution Approach 2:
Instead of testing with the entire audience, the system performs partial testing with a representative subset of historical audience members. This partial action approach detects the majority of potential issues while consuming significantly fewer resources and time than full-scale testing, achieving an optimal balance between reliability verification and resource efficiency.
Data Source
AI summary
A method includes receiving analytics data from a first plurality of network components of a stream network. The analytics data is associated with the first plurality of network components streaming first chunks of a first live stream. The method also includes determining a network parameter based on the analytics data. The network parameter includes one or more live stream parameters, a distribution plan, or a combination thereof. The method further includes reconfiguring, based on the network parameter, at least a first network component of the first plurality of network components. Second chunks of the first live stream are streamed by a second plurality of network components subsequent to the reconfiguring of the first network component.