Network Optimization System for Streaming Quality
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Solution Overview
Problem
Existing media streaming technologies face challenges in determining optimal network settings to improve quality of experience, particularly in adjusting for varying internet conditions and device types, leading to issues like buffering and low quality of experience due to factors like latency and throughput.
Innovation Solution
A network optimization system that uses clustering and classification algorithms to analyze network data from multiple devices, determine network groups, and adjust settings such as bitrate and latency to enhance streaming quality, dynamically adapting to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network and device settings are adjusted manually to improve streaming quality, then streaming quality may be improved for a given device and network, but it becomes difficult to determine which parameters to adjust and to dynamically adapt to changing conditions
Solution Approach 1:
The system enables self-service by automatically analyzing network conditions and device characteristics to determine optimal streaming parameters without manual intervention. The server performs classification and clustering operations to autonomously adjust bitrate, resolution, and other settings based on real-time conditions
Solution Approach 2:
The system dynamically changes multiple parameters including bitrate, resolution, buffer size, and latency settings based on classified network conditions. Different parameter sets are applied to different network classes to optimize streaming quality for varying network environments
2Adaptability or versatility
If the number of devices and network varieties increase, then system versatility improves, but the difficulty of determining optimal settings increases significantly
Solution Approach 1:
The system segments the vast diversity of devices and networks into manageable clusters based on shared characteristics. By grouping similar devices and network conditions together, the system reduces the complexity of determining optimal settings for each individual device while maintaining high adaptability
Solution Approach 2:
The classification and clustering models serve multiple functions: they identify network conditions, predict streaming quality, determine optimal parameters, and adapt to new device types. This multi-functionality allows the system to handle diverse devices and networks with a unified approach
3Stability of the object's composition
If buffering is increased to prevent interrupted viewing, then viewing session continuity improves, but buffering time increases causing low quality of experience
Solution Approach 1:
The system dynamically adjusts buffer size and bitrate based on real-time network conditions and device capabilities. Rather than using fixed buffering strategies, the system adapts parameters on-the-fly to maintain viewing continuity while minimizing buffering time and maximizing quality of experience
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for optimizing network performance on a computer device to improve quality of experience by determining which network settings on the computing device to adjust. A clustering algorithm may identify various classes of networks and a classification algorithm may determine a network class specific to a network on a computing device. The effects of certain network settings for that networks class may be determined and the network setting and/or settings that optimizes the network performance may be promoted. The system may periodically analyze network data to recalculate the appropriate networks class and may determine different network settings based on the recalculation, facilitating mid-session improvements to the quality of experience.