Buffer Modulation for Call Quality Over Best-Effort Networks
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Solution Overview
Problem
Current call over network systems face connectivity issues due to data transmission over 'best effort' networks, leading to reduced call quality and frequent dropped calls, especially in sub-optimal network conditions, which hampers communication efficiency and productivity.
Innovation Solution
Implementing systems and methods that include buffer length modulation based on call scenarios and deploying intermediary modules across the network to intercept and recover lost audio packets, ensuring high call fidelity and reducing latency, while maintaining acceptable levels of user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted over best effort network, then network coverage and accessibility are improved, but call quality and timing are negatively impacted
Solution Approach 1:
The system performs preliminary actions by proactively detecting network conditions and pre-adjusting buffer lengths before call quality deteriorates. The scenario-based buffer modulation mechanism continuously monitors network status and modifies buffering parameters in advance to prevent timing issues and quality degradation, rather than reacting after problems occur.
Solution Approach 2:
The system implements dynamic adaptation by continuously adjusting buffer lengths based on detected call scenarios and network conditions. Different buffer configurations are applied dynamically for different scenario types (e.g., voice calls, video calls, data transfers), allowing the system to optimize call quality adaptively while maintaining network accessibility.
2Reliability
If buffer length is increased to improve call quality, then timing and quality are improved, but latency increases
Solution Approach 1:
The system applies local quality by implementing scenario-specific buffer lengths rather than using a uniform buffer configuration for all call types. Each call scenario (voice, video, data) receives optimized buffer parameters tailored to its specific requirements, allowing high call quality for scenarios that need it while minimizing latency for time-sensitive scenarios.
Solution Approach 2:
The system changes buffer length parameters dynamically based on detected call scenarios and network conditions. By modifying the buffer length parameter adaptively rather than maintaining a fixed value, the system achieves high call quality when needed while reducing latency for scenarios requiring faster response times.
3Loss of energy
If existing systems reduce data demanding communications to handle poor network conditions, then network resource usage is reduced, but call quality and communication efficiency deteriorate
Solution Approach 1:
The system performs preliminary detection of network conditions and call scenarios, then proactively applies appropriate buffer configurations before communication quality deteriorates. This allows the system to maintain high call quality and communication efficiency even in sub-optimal network conditions by preparing the right buffer settings in advance rather than reducing data transmission.
Data Source
AI summary
Systems and methods for improving quality of a call over network (CON) are provided. 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.