Decision-Tree Video Transcoding for Selective Bitrate Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing video transcoding process consumes significant transcoding resources and requires multiple servers, leading to increased costs due to the need to transcode videos at multiple bit rate levels.
Innovation Solution
A method and apparatus for video transcoding that utilizes a decision tree regression model to predict the play count at different bit rate levels, allowing for the prioritization of transcoding based on predicted play counts, thereby reducing the need to transcode all levels simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If videos are transcoded at multiple bit rate levels, then user viewing experience is improved, but transcoding resources and device costs increase significantly
Solution Approach 1:
The system performs preliminary prediction of video play counts at different bit rate levels using a decision tree regression model before actual transcoding occurs. This allows the system to pre-determine which bit rate levels are most likely to be viewed, enabling prioritization of transcoding resources toward those levels, thereby reducing overall resource consumption while maintaining user experience.
Solution Approach 2:
The system changes the parameter of transcoding scope from all bit rate levels to selected bit rate levels based on predicted play counts. By dynamically adjusting which bit rate levels are transcoded based on prediction results, the system optimizes the balance between user viewing experience and resource consumption.
2Reliability
If all bit rate levels are transcoded simultaneously, then user viewing experience is ensured, but device costs and transcoding complexity increase
Solution Approach 1:
The system performs preliminary prediction of video play counts using a decision tree regression model before transcoding. This prediction step identifies which bit rate levels are most likely to be viewed, allowing the system to prioritize transcoding those levels first, thereby reducing the complexity of managing multiple bit rate levels simultaneously.
Solution Approach 2:
Instead of transcoding all bit rate levels (excessive action), the system selectively transcodes only the necessary bit rate levels based on predicted play counts (partial action). This approach ensures adequate user viewing experience for the most popular content while avoiding the complexity and cost of transcoding less popular content.
3Productivity
If multiple servers are deployed for transcoding, then video delivery capability is improved, but costs increase
Solution Approach 1:
The system performs preliminary prediction of video play counts at different bit rate levels before transcoding occurs. This allows a single server to intelligently prioritize which videos and which bit rate levels to transcode first, reducing the need for multiple servers to handle all possible bit rate levels simultaneously.
Solution Approach 2:
The system changes the parameter of transcoding scope from all bit rate levels to selected bit rate levels based on predicted play counts. This dynamic adjustment allows a smaller number of servers to handle the same video delivery workload by focusing resources on the most popular content and bit rate levels.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an apparatus, a device, and a storage medium for video transcoding. The method includes: obtaining a first video to be transcoded; determining first video feature information corresponding to the first video; determining, based on the first video feature information and a predetermined decision tree regression model, a predicted play count of the first video at each of bit rate levels that are currently not transcoded; and determining a target bit rate level from the bit rate levels based on the predicted play count, and transcoding the first video based on the target bit rate level.