Video Preloading via Segment Selection
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Solution Overview
Problem
Conventional video loading methods fail to enable quick playback of fast-forwarded video files due to slow network speeds and high definition file requirements, leading to poor user experience as users often have to wait for the video to load before resuming playback at the desired node.
Innovation Solution
A video loading method that segments a video into multiple segments based on historical user behavior, determining a target segment with a high search rate and preloading it to ensure smooth playback when users fast-forward, thereby reducing wait times and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional video loading methods are used, then the video file is loaded in its entirety, but the video playback is slow when fast-forwarding due to network speed limitations and high definition file requirements
Solution Approach 1:
The video file is divided into multiple segments based on historical user behavior data. The system identifies high-probability segments that users are likely to fast-forward to and pre-loads only those specific segments rather than the entire video file, enabling faster playback resumption when users fast-forward.
Solution Approach 2:
The system performs preliminary actions by pre-loading video segments before the user actually requests them. Based on historical search rate data, the system proactively loads segments into memory in advance, so when users fast-forward to those segments, playback resumes immediately without waiting for loading.
2Reliability
If the entire video file is preloaded, then playback can be smooth, but network bandwidth is wasted loading segments that users may not watch
Solution Approach 1:
Instead of uniformly preloading the entire video file, the system applies local quality by selectively pre-loading only specific segments that have high historical search rates. Each segment is treated differently based on its likelihood of being watched, optimizing bandwidth usage while maintaining playback smoothness for the most probable segments.
Solution Approach 2:
The system performs partial action by loading only a portion of the video file - specifically the segments with the highest probability of being fast-forwarded to. This partial preloading approach provides sufficient playback smoothness for the most likely scenarios without the excessive bandwidth consumption of loading the entire file.
3Productivity
If video segments are segmented and selectively preloaded based on historical behavior, then fast-forwarded video playback is quick, but the system complexity increases
Solution Approach 1:
The system uses historical user behavior data that already exists from previous video watching sessions. By analyzing this self-generated data, the system automatically identifies patterns in user fast-forward behavior and uses that information to guide segment selection, reducing the need for complex real-time analysis while improving loading efficiency.
Data Source
AI summary
The present disclosure provides techniques of preloading video data. The techniques comprises acquiring a video to be played; acquiring information indicative of historical behaviors of users who watched the video; segmenting the video into a plurality of video segments; determining a historical search rate corresponding to each of the plurality of video segments based on the information indicative of the historical behaviors of the users; and determining a video segment among the plurality of video segments as a first target video segment based on the historical search rate corresponding to each of the plurality of video segments, wherein the video segment has a historical search rate greater than or equal to a preset probability threshold; and preloading the first target video segment of the video.