Adaptive Video Preloading from Historical Stuttering Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing preloading methods for video playback optimize user experience but result in data waste due to excessive caching of videos that are not watched or require more data than necessary, without considering historical playback quality.
Innovation Solution
A preloading method that determines a stuttering coefficient based on historical stuttering information, adjusts a target preloading parameter based on a comparison with threshold values, and dynamically adjusts preloading data based on these parameters to optimize video playback quality while reducing data waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preloading data is increased to optimize video playback quality, then playback quality is improved, but data waste increases
Solution Approach 1:
The patent applies dynamics by making the preloading parameter adjustable based on historical stuttering information. The system dynamically changes the preloading strategy from a fixed approach to an adaptive one, where the preloading parameter is modified according to actual playback quality history, allowing optimization of both playback quality and data efficiency
Solution Approach 2:
The patent changes the preloading parameter based on the stuttering coefficient calculated from historical stuttering information. By modifying this parameter dynamically, the system adjusts the amount of data preloaded to match actual user needs, reducing data waste while maintaining playback quality
2Reliability
If preloading parameter is fixed to ensure consistent playback quality, then playback quality is maintained, but adaptability to historical patterns is reduced
Solution Approach 1:
The patent implements feedback by using historical stuttering information to calculate a stuttering coefficient, which then influences the preloading parameter selection. This closed-loop feedback mechanism allows the system to learn from past performance and adapt future preloading strategies accordingly, maintaining both consistency and adaptability
Data Source
AI summary
A preloading method, an electronic device and a medium are provided. The method includes: determining a stuttering coefficient based on historical stuttering information of an application; determining a target preloading parameter based on a comparison result between the stuttering coefficient and a set threshold value; and preloading data in the application based on the target preloading parameter.