Call Quality Optimization via Pathway Testing and Buffer Modulation
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Solution Overview
Problem
Current call over network systems face issues with connectivity and call quality due to data transmission over 'best effort' networks, leading to reduced fidelity and frequent call drops, especially in sub-optimal conditions, which affects communication efficiency and participant engagement.
Innovation Solution
The system employs variable buffering and network pathway testing to optimize call quality, along with diagnostic capabilities for the audio pathway, and integrates call visualization features to enhance participant understanding and reduce latency, allowing calls to continue under sub-optimal conditions without dropping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted over best effort network, then network adaptability is improved, but call quality and timing are worsened
Solution Approach 1:
The system performs network pathway testing before the call to identify and select the optimal transmission path in advance. This preliminary action ensures that when the call occurs, the best available network route is already established, preventing quality degradation during actual communication.
Solution Approach 2:
The system introduces an intermediary diagnostic capability that continuously monitors the audio pathway and network conditions. This intermediary layer detects issues before they severely impact call quality and enables dynamic adjustment of transmission parameters to maintain reliability over best effort networks.
2Device complexity
If video quality is reduced or call is dropped in sub-optimal conditions, then device complexity is reduced, but call reliability is worsened
Solution Approach 1:
The system dynamically adjusts video quality and audio buffering based on real-time network conditions rather than using fixed reduction rules. This dynamic adaptation allows the system to maintain calls in sub-optimal conditions by intelligently modulating quality parameters, preventing unnecessary call drops while avoiding the complexity of multiple fixed-quality modes.
Solution Approach 2:
The system changes transmission parameters such as bitrate, resolution, and buffering duration based on network conditions. By continuously adjusting these parameters rather than simply reducing quality or dropping calls, the system maintains call continuity while adapting to varying network capabilities.
3Reliability
If latency is increased to improve call fidelity, then call quality is improved, but communication efficiency is worsened
Solution Approach 1:
The system performs audio pathway diagnostics and establishes optimal transmission paths before the call begins. This preliminary setup minimizes latency from the start while ensuring high fidelity transmission routes are pre-configured, avoiding the need for latency-inducing adjustments during the call.
Solution Approach 2:
The system implements real-time feedback mechanisms that monitor both call fidelity and latency metrics. Based on this feedback, the system dynamically adjusts buffering and transmission parameters to maintain the optimal balance between quality and efficiency, preventing excessive latency accumulation while preserving call fidelity.
Data Source
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AI summary
Systems and methods for improving quality of a call over network (CON) are provided. Call quality may be improved via pathway testing to determine data path quality. This may be utilized to inform buffering lengths, and also may be utilized to choose the data pathway utilized for transmitting the data. Pathway testing may employ collecting microphone data on one device, transmitting it across the various pathways, and then comparing the quality at the endpoint compared to the initial data. Additionally, call quality may be improved via buffer length modulation based upon the call scenario type. Scenario detection may be based upon who speaks, and the duration of the speaking, as well as contextual analysis. Further, the call over network quality may further be improved by deploying modules over the network. The modules are intermediary vehicles between each communicator and backend servers. The modules intercept audio packets from the communicator to detect packet loss, and perform recovery of lost packets, thereby accelerating real-time audio conversations.