Adaptive Bitrate Selection Using Playback History
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Solution Overview
Problem
Existing adaptive bitrate streaming technologies face challenges in providing an optimal streaming experience due to issues like buffering and lower-than-expected video quality, often resulting from suboptimal initial bitrate selection that does not consider available bandwidth.
Innovation Solution
An adaptive bitrate algorithm that uses historical information from previous playback sessions to determine a more optimal bitrate for initial and subsequent profile selection, adjusting based on network conditions and historical metrics such as bandwidth and round trip time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional adaptive bitrate algorithms are used without considering historical information, then the algorithm complexity remains low, but the bitrate selection becomes suboptimal resulting in buffering and lower video quality
Solution Approach 1:
The system performs preliminary actions by storing historical bandwidth and playback information before the actual bitrate selection is needed. This historical data is prepared in advance and retrieved when making bitrate decisions, allowing the algorithm to make more informed selections without significantly increasing real-time computational complexity.
Solution Approach 2:
The algorithm incorporates feedback from historical playback sessions by analyzing stored bandwidth measurements and playback outcomes. This feedback loop allows the system to learn from past performance and adjust bitrate selections accordingly, improving reliability while maintaining manageable complexity through efficient data utilization.
2Productivity
If historical information is stored and analyzed for bitrate selection, then bitrate selection accuracy improves reducing buffering, but storage requirements and processing time increase
Solution Approach 1:
The system extracts only the essential historical information needed for bitrate selection, such as average bandwidth and playback outcomes, rather than storing and processing all historical data. This selective extraction reduces the data volume and processing requirements while maintaining the accuracy needed for optimal bitrate selection.
Solution Approach 2:
The algorithm applies partial analysis by considering only the most relevant historical metrics (bandwidth and playback success/failure) rather than all possible historical data. This partial action approach reduces processing time while still achieving sufficient accuracy for improving playback startup speed.
3Reliability
If initial bitrate is selected without considering available bandwidth, then the selection process is simpler and faster, but buffering issues occur and video quality deteriorates
Solution Approach 1:
The system performs self-service by automatically utilizing stored historical bandwidth information to make bitrate selections without requiring manual intervention or complex real-time measurements. The historical data serves the system's own needs, enabling automatic adaptive bitrate selection that reduces buffering while maintaining operational simplicity.
Data Source
AI summary
In some embodiments, a method selects historical information from one or more previous playback sessions to a current playback session for content. A bandwidth is determined using the historical information. The method selects a profile from a list of profiles using a bitrate that is determined based on the bandwidth. The profile is used to download a segment of the content in the current playback session.