Multimedia Preloading Using Playback Behavior and Content Features
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Solution Overview
Problem
Existing multimedia pre-loading methods result in inefficient data loading, often pre-loading data that users do not want to play back, leading to wasted bandwidth and reduced efficiency.
Innovation Solution
A multimedia data pre-loading method that acquires target operation data and feature data of a user's historical playback behavior and multimedia content to determine weights and priorities for pre-loading, optimizing the data to be played back based on network status, user preferences, and content features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multimedia data is pre-loaded without user behavior analysis, then playback buffering is avoided, but bandwidth is wasted loading unwanted data
Solution Approach 1:
The system performs preliminary actions by analyzing user operation data and playback habits before actual playback occurs. It predicts which multimedia segments the user will want to play next and pre-loads only those specific segments, rather than loading all possible data. This ensures playback continuity while avoiding bandwidth waste on unwanted content.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user operation data, playback completion rates, and interaction patterns. This feedback is used to dynamically adjust pre-loading strategies, predicting future user preferences based on historical behavior. The system refines its predictions over time, improving accuracy in determining which data to pre-load, thus avoiding waste while maintaining reliable playback.
2Speed
If all multimedia data is pre-loaded, then playback buffering is eliminated, but storage and processing resources are inefficiently used
Solution Approach 1:
The system extracts only the necessary multimedia segments that are likely to be played next, based on user behavior analysis and playback patterns. Instead of loading all multimedia data, it selectively extracts and pre-loads specific portions relevant to user preferences, improving resource efficiency while maintaining fast data loading speed for actual playback.
Solution Approach 2:
The system applies local quality by differentiating between high-priority and low-priority multimedia segments. It pre-loads high-priority segments (those most likely to be played) with higher quality and completeness, while lower-priority segments are either not pre-loaded or loaded with reduced quality. This optimizes resource usage while ensuring fast loading of critical playback data.
3Quantity of substance
If pre-loading is performed without considering user preferences, then network bandwidth is consumed, but user satisfaction is not improved
Solution Approach 1:
The system implements dynamic pre-loading strategies that adapt to changing user preferences and behaviors. Instead of static pre-loading rules, it continuously updates its understanding of user preferences based on real-time operation data and adjusts pre-loading content accordingly. This ensures that the pre-loaded data volume aligns with actual user needs, improving satisfaction while consuming network bandwidth efficiently.
Solution Approach 2:
The system changes key parameters such as pre-loading quantity, data quality, and selection criteria based on user preference analysis. By dynamically adjusting these parameters according to user behavior patterns, it ensures that the pre-loaded data volume and content match user expectations, thereby improving user satisfaction without unnecessary bandwidth consumption.
Data Source
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AI summary
The present disclosure relates to a multimedia data pre-loading method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring target operation data for multimedia which have been played back; acquiring target feature data of multimedia to be played back; and at least on the basis of the target operation data and the target feature data, pre-loading the multimedia to be played back.