Parameterized Scheduling for Video Traffic Prioritization
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Solution Overview
Problem
Current communication networks face challenges in maximizing Quality of Experience (QoE) due to inadequate differentiation of data packets from heterogeneous applications, leading to unfair treatment of packets during network congestion, which results in suboptimal video and voice quality.
Innovation Solution
A parameterized scheduling system that incorporates end-user application awareness, using enhanced packet inspection and queuing techniques to dynamically manage scheduling parameters based on Application Factors (AF), 'duration neglect', and 'recency effect' to prioritize video traffic during congestion periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enhanced packet inspection and classification techniques are implemented to differentiate data packets from heterogeneous applications, then Quality of Experience (QoE) for video and voice applications is improved, but device complexity and processing requirements increase
Solution Approach 1:
The packet inspection and classification process is divided into multiple stages: initial classification based on packet headers, followed by deeper inspection only for packets requiring detailed analysis. This segmentation allows the system to handle heterogeneous traffic with differentiated processing depths, improving QoE for critical applications while reducing overall processing complexity.
Solution Approach 2:
Different packet inspection depths are applied to different types of traffic based on their QoE requirements. Video and voice packets receive enhanced inspection and classification resources, while less critical traffic uses standard processing. This local quality approach ensures reliable QoE improvement for priority applications without uniformly increasing processing complexity across all traffic types.
2Reliability
If dynamic scheduling parameter adjustment based on Application Factors is implemented to prioritize video traffic during congestion, then video quality is maintained, but processing time and computational overhead increase
Solution Approach 1:
Application Factors are pre-calculated and stored for different application types before congestion occurs. During normal operation, these factors are computed and cached. When network congestion is detected, the system directly applies the pre-computed factors to adjust scheduling parameters, maintaining video quality without the computational overhead of real-time calculation during congestion events.
Solution Approach 2:
The system continuously monitors network conditions and packet characteristics, using this feedback to dynamically adjust scheduling parameters based on pre-established Application Factors. This feedback mechanism allows the system to respond to congestion events efficiently by applying predetermined optimization rules, maintaining video quality while minimizing processing time through rule-based decision making rather than complex real-time optimization.
Data Source
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AI summary
A communication device comprises a parameterized scheduling module configured to receive a plurality of packets and a packet inspection module, wherein the packet inspection module comprises: a traffic monitoring module configured to filter the plurality of packets according to a packet feature; a connection detection module configured to receive a plurality of first filtered packets; a stream and session detection module configured to detect information about streams, sessions, and application associated with the filtered packets; and a status module configured to store the information, wherein the traffic monitoring module filters the plurality of packets according to the information and transmits a plurality of second filtered packets to a server.