Reinforcement Learning Jitter Buffer Control
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Solution Overview
Problem
Media streaming over networks often experiences jitter, leading to audio quality issues due to varying packet delays, which existing technologies struggle to fully mitigate without introducing noticeable latency or degrading playback quality.
Innovation Solution
A reinforcement learning-based system that uses a machine-learned model to dynamically adjust jitter buffer delay by inputting current jitter buffer and network status information, trained with network traces from past streaming sessions to optimize the decision-making process, allowing for stretch, compress, or hold actions on media frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If jitter buffer delay is increased to compensate for network jitter, then audio quality is improved, but latency increases and interactivity is degraded
Solution Approach 1:
The jitter buffer delay is made dynamic rather than static. The system continuously monitors network conditions (packet arrival intervals, jitter levels) and adjusts the buffer delay accordingly. When network conditions are good, delay is reduced to minimize latency; when conditions deteriorate, delay is increased to maintain audio quality, thus resolving the contradiction between reliability and time loss
Solution Approach 2:
The system implements feedback mechanisms by monitoring network conditions and audio quality metrics, then using this information to adjust jitter buffer delay in real-time. This closed-loop control allows the system to adapt to changing conditions and optimize the balance between audio quality and latency
2Loss of time
If jitter buffer delay is decreased to reduce latency, then interactivity is improved, but audio quality deteriorates due to playback gaps
Solution Approach 1:
The system dynamically adjusts jitter buffer delay based on real-time network conditions. When network conditions are favorable (low jitter, steady packet arrival), the buffer delay is decreased to reduce latency and improve interactivity. When conditions worsen, delay is increased to prevent playback gaps, thus resolving the contradiction between time loss and reliability
Solution Approach 2:
The system changes the delay parameter of the jitter buffer based on network conditions. By monitoring packet arrival intervals and jitter levels, the system adjusts the delay parameter to optimize the balance between latency and audio quality, preventing playback gaps while minimizing delay when possible
3Ease of operation
If traditional jitter buffer management is used, then basic jitter compensation is achieved, but audio quality issues persist under varying network conditions
Solution Approach 1:
The system employs machine learning models that automatically learn optimal jitter buffer management strategies from network traces and performance data. The model self-adjusts buffer parameters based on learned patterns, eliminating the need for manual configuration and adapting to diverse network conditions, thus improving audio quality while maintaining ease of operation
Solution Approach 2:
The patent replaces traditional rule-based or manually configured jitter buffer management with a machine learning-based system. The ML model processes network conditions and automatically determines optimal buffer delay settings, substituting mechanical/manual adjustment with intelligent automated decision-making that adapts to varying network conditions
Data Source
AI summary
Disclosed in some examples are methods, systems, and machine-readable mediums which determine jitter buffer delay by inputting jitter buffer and currently observed network status information to a machine learned model that is trained using a reinforcement learning (RL) method. The model maps these inputs to an action to compress, stretch, or hold the jitter buffer delay, which is used by a recipient computing device to optimize the jitter buffer delay. The model may be trained using a simulator that uses network traces of past real streaming sessions (e.g., communication sessions) of users. By training the model through reinforcement learning, the model learns to make better decisions through reinforcement in the form of reward signals that reflect the performance of each decision.