Video Playback Quality Scheduling for Throughput Fluctuations
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Solution Overview
Problem
Existing video playback techniques focus on short-term chunk selection, failing to anticipate network degradation and resulting in non-optimal user experience due to frequent quality fluctuations and stalling.
Innovation Solution
A video playback method that utilizes long-term throughput prediction to determine a quality adaptation schedule by configuring a search space for future time intervals, evaluating multiple paths, and selecting the path with the highest quality of experience, allowing for smoother quality adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional short-sighted chunk selection is used, then the video player can respond to observed throughput degradations, but the quality of experience deteriorates due to frequent quality changes and stalling
Solution Approach 1:
The patent applies preliminary action by predicting future throughput values before they occur and proactively adjusting quality adaptation schedules in advance. Instead of reacting to observed degradations, the system forecasts network conditions and pre-configures quality changes to maintain smooth playback and avoid stalling events.
Solution Approach 2:
The system dynamically adjusts the quality adaptation schedule based on predicted throughput variations. The chunk selection mechanism becomes adaptive and flexible, continuously optimizing quality decisions based on forecasted network conditions rather than following fixed or reactive rules.
2Reliability
If long-term throughput prediction is implemented, then future network changes can be anticipated and quality can be maintained, but the computational complexity increases
Solution Approach 1:
The patent segments the future time horizon into discrete time intervals and evaluates multiple possible paths through the search space separately. This segmentation allows the system to manage computational complexity by breaking down the prediction problem into smaller, more tractable sub-problems that can be solved efficiently.
Solution Approach 2:
The system performs partial action by evaluating only the most promising paths through the search space rather than exhaustively analyzing all possible quality adaptation schedules. This selective evaluation reduces computational burden while still achieving near-optimal quality consistency.
3Adaptability or versatility
If frequent quality adjustments are made to match throughput changes, then the video quality can adapt to network conditions, but user experience deteriorates due to frequent quality changes
Solution Approach 1:
The system performs preliminary quality adjustments based on predicted throughput changes before they occur. By anticipating network conditions and proactively configuring quality schedules, the system avoids abrupt reactive quality changes that would be perceived as frequent fluctuations by users, thereby maintaining both adaptability and stability.
Data Source
AI summary
A video player includes a processor; and a memory that includes instructions, which when executed, cause the processor to execute the following steps: predicting a future throughput by using a past throughput value; configuring a search space for identifying a quality adaptation schedule; extracting a plurality of paths in the search space; estimating, based on the future throughput, a quality of experience value for each path of the extracted plurality of paths; identifying a path corresponding to a highest quality of experience value from among the estimated quality of experience values, as the quality adaptation schedule; determining, based on the quality adaptation schedule, a quality value of a chunk to be requested for the future unit time interval subsequent to the current unit time interval; requesting the chunk with the determined quality value; and receiving the chunk with the determined quality value.