AI/ML Adaptive Bitrate Control for Fluctuating Networks
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Solution Overview
Problem
Conventional adaptive bitrate streaming (ABR) technologies face challenges in providing a seamless viewing experience due to their reliance on simplistic algorithms that fail to dynamically adjust to fluctuating network conditions, leading to buffering, pauses, and drops in video quality.
Innovation Solution
Implementing a streaming controller with AI/ML models to continuously monitor network and device status, enabling real-time optimization of streaming parameters such as bitrate, thereby enhancing bandwidth efficiency and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional ABR algorithms are used to select bitrate based on network bandwidth and buffer occupancy, then the system is simple to implement, but the viewing experience deteriorates with buffering, pauses, and quality drops
Solution Approach 1:
The patent replaces conventional rule-based ABR algorithms with machine learning models that dynamically predict optimal bitrates. The ML models analyze historical and real-time network conditions, device characteristics, and user behavior patterns to make intelligent bitrate selections, substituting simple mechanical decision rules with adaptive intelligent systems that continuously learn and improve performance
Solution Approach 2:
The system transitions from static bitrate selection based on fixed thresholds to dynamic bitrate adaptation using ML models that continuously adjust predictions based on changing network conditions, device states, and user preferences. The ML models process real-time data streams and adapt their predictions dynamically, enabling seamless viewing experience despite fluctuating network conditions
2Device complexity
If simplistic ABR algorithms are used, then the system complexity is low, but the ability to capture complex network dynamics is insufficient
Solution Approach 1:
The patent replaces simple algorithmic approaches with machine learning models capable of capturing complex non-linear relationships in network behavior. The ML models process multiple features including network throughput, latency, packet loss, device performance metrics, and user interaction patterns, enabling the system to adapt to complex dynamic conditions that simplistic algorithms cannot handle
Solution Approach 2:
The system combines multiple data sources and features into a composite input for the ML models, integrating network conditions, device characteristics, and user behavior data. This composite approach enables the system to capture multifaceted dynamics of streaming environments, similar to how composite materials combine different properties to achieve superior performance
3Ease of manufacture
If fixed bitrate encoding is used, then the encoding process is simple, but the system cannot dynamically adjust to real-time network conditions
Solution Approach 1:
The system performs preliminary encoding of video content at multiple fixed bitrates using conventional methods, then uses ML models to intelligently select and switch between these pre-encoded variants in real-time. This approach maintains the simplicity of fixed-rate encoding while adding adaptive selection capability, allowing the system to balance encoding complexity with runtime adaptability
Data Source
AI summary
Systems, devices, and methods related to media streaming are provided. An example media streaming system includes a media server connected to a network and a bitrate controller connected to the network. The media server is configured to transmit a media stream to a client device connected to the network in a sequence of successive time periods along a chronological timeline. The bitrate controller is configured to continuously monitor the network and obtain real-time network performance data indicating a current status of the network for each time period, obtain real-time operating status data indicating a current operating status of the client device for each time period, determine a bitrate for each time period, based on the network performance data and the operating status data, and cause the media server to transmit the media stream to the client device at the determined bitrate for each time period.