Dynamic Bit Rate Allocation for Video Distribution Stability
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Solution Overview
Problem
Conventional video coding systems face challenges in maintaining quality of experience (QoE) for users due to limited bit rate settings and inability to adapt to varying network throughput, leading to inefficient resource utilization and decreased QoE when network conditions change.
Innovation Solution
A coded data generation method and apparatus that dynamically adjusts bit rates based on evaluated user experience values across multiple channels, ensuring optimal bit rate allocation by acquiring and aggregating quality of experience values from playback terminals and adjusting bit rates accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a larger number of bit rates are used to perform stable video distribution in varying network conditions, then video distribution stability is improved, but encoder processing load and facility cost increase
Solution Approach 1:
The system dynamically adjusts the number of bit rate channels based on actual network conditions and user feedback. Instead of maintaining a fixed large number of channels, the encoder adapts the channel configuration in real-time, increasing channels when network stability improves and decreasing them when conditions deteriorate or processing load becomes excessive.
Solution Approach 2:
The invention changes the parameter of bit rate channel configuration from a static fixed value to a dynamic variable that adjusts based on network throughput measurements and user quality feedback. This allows the system to optimize between distribution stability and processing capacity by modifying the number and spacing of bit rate channels according to actual operating conditions.
2Device complexity
If bit rates are set in fixed stages from several hundred kbps to several ten Mbps, then device complexity is reduced, but quality of experience deteriorates when network throughput falls between bit rate stages
Solution Approach 1:
The system implements feedback loops where user quality of experience measurements and network throughput data are continuously collected and used to adjust bit rate channel configurations. This feedback mechanism allows the system to identify gaps between fixed bit rate stages and insert additional channels where needed, improving QoE without requiring complete redesign of the entire bit rate structure.
Solution Approach 2:
Instead of uniformly distributing bit rate channels across the entire range from several hundred kbps to several ten Mbps, the system applies local quality enhancement by inserting additional bit rate channels specifically in ranges where user feedback indicates quality degradation. This allows targeted improvement of QoE in critical regions while maintaining the simplicity of fixed-stage configuration in other regions.
3Productivity
If a small number of channels are available, then encoder processing load is reduced, but quality of experience significantly changes with small throughput differences
Solution Approach 1:
The system dynamically adjusts the number of bit rate channels based on actual network conditions and user feedback. Instead of maintaining a fixed small number of channels, the encoder adapts the channel configuration in real-time, increasing channels when network stability improves and processing capacity allows, and decreasing them when conditions deteriorate or processing load becomes excessive.
Data Source
AI summary
A coded data generation method of generating coded data of a plurality of bit rates in which an image is encoded includes the steps of: acquiring an evaluated value at distribution destinations of the generated coded data when the coded data is reproduced; and changing the bit rate of the coded data to be generated based on a distribution of the acquired evaluated value in all channels.