Video Asset Delivery System Optimizing Control Parameters
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Solution Overview
Problem
Creators of video assets face inefficiencies in achieving specific goals, such as maximizing revenue or promoting ideas, due to the labor-intensive process of trial and error in adjusting control parameters like paywall locations and advertisement layouts, which often miss optimal revenue-increasing parameters.
Innovation Solution
A system that allows video asset owners to specify target control parameters and goals, automatically adjusting these parameters based on feedback metrics to optimize video delivery, using metadata, usage results, and user information to determine optimal settings for parameters like paywall duration, advertisement frequency, and layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error testing is used to adjust control parameters like paywall locations, then the video delivery can be optimized to achieve goals such as maximizing revenue, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system enables automated self-optimization of video delivery parameters by using feedback metrics to automatically adjust control parameters such as paywall locations, advertisement layouts, and preview window durations, eliminating the need for manual trial-and-error testing while continuously improving goal achievement
Solution Approach 2:
The system implements automated feedback loops where usage data and performance metrics are collected, analyzed, and used to automatically adjust delivery parameters, replacing labor-intensive manual testing with an efficient automated optimization process that maintains high reliability
2Manufacturing precision
If multiple control parameters are tested simultaneously, then more optimal settings can be discovered, but the complexity of managing and analyzing the tests increases significantly
Solution Approach 1:
The system segments the optimization process into distinct phases: first identifying which control parameters have the greatest impact on goals, then focusing testing efforts on those specific parameters while holding others constant, thereby achieving high optimization precision without overwhelming complexity
Solution Approach 2:
The system automatically determines which control parameters to vary based on their potential impact on goal achievement, dynamically adjusting the testing strategy to focus on the most influential parameters rather than testing all parameters simultaneously, reducing complexity while maintaining precision
3Loss of information
If sequential testing of different paywall locations is performed, then revenue impact can be measured, but time is lost and viewers may be lost during the testing periods
Solution Approach 1:
The system performs preliminary analysis to identify the most impactful control parameters before conducting full optimization tests, allowing for more efficient testing that requires less time while still capturing accurate revenue data by focusing on the most critical variables
Solution Approach 2:
The automated optimization system operates continuously without interruption, eliminating the stops and starts inherent in sequential manual testing, thereby reducing time loss while maintaining accurate revenue measurement through uninterrupted data collection and adjustment
4Ease of operation
If the focus is exclusively on testing paywall durations, then the testing process is simplified, but other important parameters like video player layout that could significantly affect revenue are missed
Solution Approach 1:
The system is designed to handle multiple types of control parameters universally, including but not limited to paywall durations, advertisement layouts, and video player configurations, allowing comprehensive optimization across all relevant parameters while maintaining ease of operation through automated parameter selection and testing management
Data Source
AI summary
A system is provided that facilitates achieving a goal associated with a particular video asset. The system may provide an interface through which a user may specify control parameters that are to be the targets of testing, and a goal or combination of goals. The system may control a controller that performs experiments in an attempt to identify optimal values, relative to the specified goals, for the control parameters. The optimal values may be determined and tested on a per-individual-video asset basis. Further, the controller may generate multiple sets of optimal values for a given video, where each set is associated with a different combination of request attributes. To estimate the optimal parameter values for one video, the controller may use usage information collected for that video, as well as usage information collected for similar videos.