Adaptive OTT Optimization Platform for QOE and Cost Trade-offs
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Solution Overview
Problem
Current Over-the-Top (OTT) content delivery systems lack adaptive mechanisms to optimize Quality of Experience (QOE) across optical, packet, and compute layers, leading to higher costs and lower performance due to inadequate visibility and control over network configurations, prompting content providers to work around issues at greater expense.
Innovation Solution
An adaptive OTT content optimization platform that receives network, service, and QOE inputs to analyze and adjust network configurations automatically, including physical and virtual elements across optical, packet, and compute layers, to optimize QOE by evaluating tradeoffs in revenue, cost, resource use, and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content providers manually select alternate CDNs or work around network issues, then QOE can be improved, but costs increase significantly
Solution Approach 1:
The system implements continuous feedback loops where QOE metrics from end users are collected, analyzed, and used to automatically adjust network configurations. This closed-loop feedback enables the network to self-optimize based on actual user experience data, eliminating the need for expensive manual workarounds while maintaining high QOE.
Solution Approach 2:
The network system performs self-optimization by automatically analyzing QOE data and adjusting its own configurations without requiring external manual intervention. The system autonomously identifies performance issues and implements corrective actions, replacing costly manual CDN selection with automated self-service optimization.
2Reliability
If the network lacks visibility and control over optical, packet, and NFV configurations, then operational simplicity is maintained, but QOE optimization performance deteriorates
Solution Approach 1:
The system introduces an intermediary optimization layer that sits between the content providers and the complex network infrastructure. This intermediary collects QOE data, analyzes performance issues, and translates user experience metrics into appropriate network configuration adjustments, providing controlled visibility into the complex multi-layer network without requiring end users to directly manage it.
Solution Approach 2:
The optimization system performs multiple functions across different network layers (optical, packet, NFV) through a unified platform. It simultaneously monitors QOE, analyzes performance data, identifies bottlenecks, and adjusts configurations across all layers, consolidating what would otherwise require separate specialized systems into one multi-functional solution.
3Loss of information
If QOE data is collected from end users, then visibility into user experience is improved, but system complexity increases
Solution Approach 1:
The system implements structured feedback mechanisms where end user devices automatically report QOE metrics to the optimization platform. This feedback loop provides continuous visibility into actual user experience without requiring manual surveys or complex monitoring infrastructure, as the data collection is integrated into the normal service delivery flow.
Solution Approach 2:
End user devices perform self-measurement of QOE metrics and automatically report this data to the optimization system. This self-service approach enables comprehensive QOE visibility without requiring the service provider to deploy complex monitoring agents or manual collection mechanisms at each user location.
Data Source
AI summary
Systems and methods for adaptive Over-the-Top (OTT) content optimization based on Quality of Experience (QOE) are implemented in an OTT optimization platform communicatively coupled to a plurality of devices in a network. The system and methods include receiving a plurality of inputs comprising network inputs, service and software inputs, and QOE inputs; analyzing the plurality of inputs with respect to one or more OTT content streams to perform an optimization thereof; determining adjustments in the network based on the optimization; and one of notifying a network operator of the adjustments and automatically causing the adjustments in the network.