Cloud Media Function Selection for Personalized Video Processing
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Solution Overview
Problem
Current cloud systems lack a mechanism or interface to incorporate user- or third-party-provided media processing solutions, limiting personalized media processing capabilities and user experience in scenarios like VR and point cloud applications.
Innovation Solution
Implement an interaction mechanism in the cloud system using a Hook API and function repository uploading API to enable users and third parties to upload and update media processing functions, allowing the system to select or update functions based on requirements and priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video data is transmitted at high definition quality, then image quality is improved, but bandwidth consumption increases
Solution Approach 1:
The system dynamically changes the encoding parameters (resolution, bitrate, frame rate) of video data based on network conditions and device capabilities. When bandwidth is limited, it switches to lower resolution or compressed formats; when bandwidth is abundant, it transmits high definition data, thus adapting image quality and bandwidth consumption to current conditions
Solution Approach 2:
The media processing system implements dynamic adaptation by continuously monitoring network status and adjusting video encoding parameters in real-time. This allows the system to optimize the balance between image quality and bandwidth consumption according to changing conditions, rather than using fixed transmission parameters
2Productivity
If multiple media files are processed simultaneously, then processing efficiency is improved, but memory resources are consumed
Solution Approach 1:
The system segments media files into smaller chunks or blocks for processing. Instead of loading entire files into memory, it processes segments sequentially or in parallel small batches, reducing peak memory usage while maintaining overall processing throughput through efficient segment management
Solution Approach 2:
The system processes media files partially in parallel with selective loading. It loads only the portions of files currently needed for processing into memory while keeping other portions on storage, using memory-efficient streaming techniques to maintain processing efficiency without requiring full file loading
3Measurement precision
If video resolution is increased, then display quality is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts video resolution and encoding parameters based on device power state, battery level, and performance requirements. When power saving is needed, it transmits lower resolution video; when power is abundant, it transmits high resolution video, thus adapting display quality and power consumption to current energy conditions
Solution Approach 2:
The media processing system implements dynamic resolution scaling that adapts to real-time power conditions and display requirements, optimizing the balance between display quality and power consumption rather than using fixed resolution transmission
4Speed
If media files are stored locally, then access speed is improved, but storage space is consumed
Solution Approach 1:
The system implements selective local caching where frequently accessed media files or frequently accessed portions of files are stored locally on the terminal device, while less frequently accessed content remains on remote servers. This provides fast access for common content while conserving local storage space
Solution Approach 2:
The system segments media content into frequently accessed and infrequently accessed portions, storing the frequent portions locally and the rest remotely. This segmentation approach optimizes the balance between local access speed and storage space utilization
Data Source
Figure 1~2
AI summary
Provided is a media processing method. The method is used for uploading a function provided by a user or a third party and obtaining media processing parameters provided by the user. The method includes the following steps: receiving the description information of the media processing and determining whether a function that fulfills the requirement of the description information exists in a system; in a case where the function that fulfills the requirement of the description information exists, selecting the function that fulfills the requirement of the description information from the system; and in a case where the function that fulfills the requirement of the description information does not exist, selecting the function from the system according to the priority or selecting the function that fulfills the requirement of the description information from outside the system. With the preceding solution, a personalized media processing of a cloud system based on the client or the third party can be implemented. In this manner, the user consumption experience and the integrity and the robustness of the system can be improved.