Adaptive Bitrate Streaming Using Quality Metrics and Buffer Data
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Solution Overview
Problem
Content delivery networks charge higher fees during peak periods due to increased data transfer, and existing adaptive bitrate algorithms prioritize higher bitrates over cost efficiency, leading to unnecessary bandwidth usage and potential rebuffering.
Innovation Solution
An adaptive bitrate algorithm that considers both bitrate and quality metrics to select profile levels, predicting peak periods and using quality determination systems to minimize perceived quality differences, thereby reducing bandwidth usage and costs during peak times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a higher profile level with higher bitrate is selected, then video quality is improved, but data transfer cost increases
Solution Approach 1:
The system changes the selection parameters from purely bitrate-based to quality metric-based decision making. By using quality metrics that predict perceived quality differences, the system can identify when higher bitrate profiles provide negligible quality improvement, thereby selecting lower bitrate profiles that reduce data transfer costs while maintaining acceptable quality
Solution Approach 2:
The system implements feedback by monitoring actual playback performance and quality metrics, then using this information to adjust future profile level selections. The quality determination system provides feedback on whether quality improvements are perceptible, allowing the adaptive bitrate algorithm to learn and optimize its profile selection strategy to balance quality and cost
2Manufacturing precision
If a higher profile level with higher bitrate is selected, then video quality is improved, but buffer data decreases leading to rebuffering
Solution Approach 1:
The system changes the decision parameters from bitrate-only to a combination of quality metrics and buffer status. By considering both quality metrics and buffer data levels together, the algorithm can make more informed decisions that balance quality improvement against playback continuity, selecting profiles that maintain sufficient buffer data while achieving perceptible quality gains
3Loss of energy
If quality metric values are used to determine profile levels, then cost efficiency is improved, but algorithm complexity increases
Solution Approach 1:
The system performs preliminary calculations by pre-determining quality metric values for different profile levels before runtime decision making. By preparing quality metric data in advance and establishing quality thresholds beforehand, the system reduces the computational burden during real-time profile selection, making the quality-based approach more efficient and less complex than real-time quality analysis would require
Data Source
AI summary
In some embodiments, a method receives quality metric values for profile levels in a plurality of profile levels for a segment of content. An available bandwidth and associated bitrates of profile levels in the plurality of profile levels are evaluated to select a first profile level. The quality metric values for profile levels in the plurality of profile levels are evaluated to select a second profile level. The method selects a profile level in the plurality of profile levels based on the first profile level and the second profile level. The profile level that is selected is requested for the segment.