Bayesian Video Bitrate Control for Engagement and Cost Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current bitrate selection algorithms for video streaming focus on maximizing Quality of Experience (QoE) or related metrics, but lack a method to directly control engagement and costs simultaneously, as engagement characteristics are unknown and vary by content, users, and services, requiring large data accumulation.
Innovation Solution
A moving image control apparatus using Bayesian optimization to calculate a quality target value for bitrate control, optimizing a utility function that balances engagement and costs through a predetermined engagement index and cost weighting, allowing for simultaneous engagement and cost control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If QoE is excessively improved, then quality of experience is improved, but costs increase
Solution Approach 1:
The patent changes the parameter being optimized from QoE alone to a utility function that balances engagement and costs. The bitrate selection algorithm adjusts video quality parameters dynamically based on network conditions and user engagement metrics, rather than continuously maximizing QoE, thereby reducing unnecessary bandwidth consumption and costs while maintaining acceptable quality levels.
Solution Approach 2:
The patent introduces dynamic adjustment of bitrate based on real-time engagement metrics and network conditions. The system continuously monitors user engagement (viewing time, interactions) and adapts the video quality parameter dynamically, transitioning from a static QoE-maximization approach to a dynamic balance between engagement and cost considerations.
2Ease of operation
If engagement characteristics are ascertained in advance, then control is possible, but characteristics are generally unknown
Solution Approach 1:
The system performs self-service by automatically collecting engagement metrics from user interactions and using this data to refine its own bitrate selection strategy. The algorithm learns from observed user behavior (viewing patterns, engagement events) and autonomously adjusts quality parameters without requiring external configuration or prior knowledge of engagement characteristics.
Solution Approach 2:
The patent implements a feedback mechanism where engagement metrics are continuously collected from user interactions and fed back into the bitrate selection algorithm. This closed-loop system uses the feedback information to adjust future bitrate decisions, enabling the system to adapt to unknown engagement characteristics over time through observed user behavior.
3Measurement precision
If data is sufficiently accumulated, then engagement characteristics can be elucidated, but engagement cannot be controlled until data is accumulated
Solution Approach 1:
The patent applies preliminary action by implementing a default bitrate selection strategy that works acceptably before any engagement data is collected. The system starts with a baseline algorithm that makes reasonable bitrate choices without requiring historical data, then progressively refines its performance as engagement metrics are accumulated, rather than waiting for data accumulation before becoming operational.
Solution Approach 2:
The system performs partial optimization by achieving acceptable engagement control with limited data rather than waiting for complete data accumulation. The bitrate selection algorithm provides sufficient performance with minimal engagement data and continues to improve incrementally as more data becomes available, avoiding the need to wait for full data collection before achieving useful control.
4Measurement precision
If bitrate is selected to maximize QoE, then quality of experience is improved, but engagement and costs cannot be controlled simultaneously
Solution Approach 1:
The patent combines multiple objectives (engagement and cost control) into a composite utility function that guides bitrate selection. Instead of optimizing for QoE alone, the system uses a composite objective function that incorporates both engagement metrics and cost considerations, allowing simultaneous control of multiple competing goals through a unified optimization framework.
Data Source
AI summary
A moving image control apparatus is a control device that realizes bitrate control in a moving image streaming service, which includes a calculation means for calculating a quality target value used for the bitrate control such that a utility function including a predetermined engagement index value is optimized through Bayesian optimization.