Cognitive Focus Model for Dynamic Video QoS Adjustment
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Solution Overview
Problem
Existing networked computing environments lack efficient methods to differentiate between varying bandwidth and packet priority needs of users, leading to inefficient resource deployment and failure to maintain market-determined Quality of Service (QoS) expectations.
Innovation Solution
A method that generates a cognitive focus of attention model based on user monitoring data to adjust actual QoS of software applications while maintaining perceived QoS, allowing for dynamic prioritization of packets and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If per-user and per-session policing techniques are deployed, then QoS prioritization can be parameterized by both application service and user subscription, but there is no way of distinguishing between varying needs of each user for bandwidth and packet priority
Solution Approach 1:
The system continuously monitors user behavior patterns, application usage, and network performance metrics to dynamically adjust QoS parameters. This feedback mechanism enables the system to distinguish between varying user needs in real-time, adapting bandwidth allocation and packet prioritization based on actual usage patterns rather than static subscriptions alone.
Solution Approach 2:
The patent implements dynamic QoS adjustment where bandwidth and priority parameters are continuously modified based on user behavior, application type, and network conditions. This dynamic approach allows the system to respond to changing user needs, transitioning from static per-session policing to adaptive resource allocation that reflects actual user requirements.
2Productivity
If known approaches for dynamically modifying QoS levels are used, then service level requirements can be mapped to QoS parameters, but the QoS adjustment does not ensure the same market-determined QoS that the packet sender and packet receiver had previously agreed upon
Solution Approach 1:
The system incorporates feedback mechanisms that monitor actual QoS delivery and compare it against agreed-upon service level requirements. This enables the system to adjust QoS parameters dynamically while ensuring market-determined QoS guarantees are maintained, correcting any deviations from agreed-upon service levels in real-time.
Solution Approach 2:
The patent employs parameter change techniques where QoS parameters such as bandwidth, latency, and packet loss thresholds are dynamically adjusted based on monitored performance and agreed-upon service levels. This ensures that market-determined QoS guarantees are maintained while optimizing resource allocation based on actual network conditions and user behavior.
3Quantity of substance
If limited network resources are deployed, then network infrastructure costs are reduced, but the varying bandwidth and packet priority needs of users are not taken into account while ensuring quality of service
Solution Approach 1:
The system implements dynamic resource allocation where network bandwidth and priority parameters are continuously adjusted based on user behavior patterns and actual usage requirements. This dynamic approach enables efficient use of limited resources while accommodating varying user needs, transitioning from static resource allocation to adaptive sharing that reflects real-time user requirements.
Solution Approach 2:
The patent employs parameter change techniques to dynamically modify bandwidth allocation and packet priority parameters based on monitored user behavior and network conditions. This enables the system to optimize resource utilization while maintaining quality of service, adjusting parameters in real-time to match actual user needs rather than relying on fixed allocation schemes.
Data Source
AI summary
An approach is provided for managing a quality of service (QoS). It is determined that a user communicates with a first video conference participant and no other participant(s). A cognitive focus of attention model is generated. The cognitive focus of attention is a region of a display presenting the video conference that includes a face of the first participant. The model specifies a peripheral vision resolution of the user. First and second actual QoS of other regions and the region, respectively, are generated. Based on the model and the first and second actual QoS, an adjustment to the first actual QoS is determined, without adjusting the second actual QoS, and while maintaining unchanged a perceived QoS. A resolution of the other regions is matched to the peripheral vision resolution by delaying or dropping packets that specify the other regions, which reduces a video bit rate of the other regions.