Multimedia Queue Frame Gradient Pruning
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Solution Overview
Problem
The rapid growth of video traffic and limitations in communication resources lead to Quality of Experience (QoE) degradation in multimedia services over the Internet, particularly due to varying network bandwidth and congestion, causing issues like blur, smear, and jerky video effects.
Innovation Solution
A system and method for managing multimedia queues by selecting candidate frames with minimal frame gradients and dropping them based on queue weights and network performance metrics, using a Video Queue Pruning Algorithm to maintain graceful QoE degradation, where frames are replaced using Mean-squared error (MSE), color histogram-based distance, or weighted sum of color change methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network bandwidth is narrow or the network is congested, then network throughput decreases, but Quality of Experience (QoE) degrades with blur, smear, and jerky video effects
Solution Approach 1:
The patent extracts and removes specific video frames (I-frames, P-frames, B-frames) from the transmission queue based on their gradient values and importance metrics. By selectively dropping less critical frames while preserving key frames, the system reduces network throughput requirements while minimizing QoE degradation, directly resolving the contradiction between throughput and quality.
Solution Approach 2:
The patent applies different quality preservation strategies to different frame types within the video stream. Critical frames (I-frames) receive higher protection and are less likely to be dropped, while less critical frames (B-frames) are more readily discarded. This local differentiation of quality requirements allows the system to maintain overall QoE while adapting to varying network throughput conditions.
2Quantity of substance
If video traffic increases rapidly, then network demand grows, but communication resources remain limited causing QoE degradation
Solution Approach 1:
The patent segments the video traffic into distinct frame categories (I-frames, P-frames, B-frames) with different importance levels and gradient characteristics. This segmentation enables selective transmission where critical segments are preserved and less critical segments are dropped during congestion, allowing the system to handle increased video traffic volume without proportionally increasing QoE degradation.
Solution Approach 2:
The patent dynamically changes transmission parameters (frame selection criteria, gradient thresholds, dropping probabilities) based on network conditions and frame characteristics. By adjusting these parameters in real-time according to traffic volume and available resources, the system can accommodate rapid video traffic growth while maintaining acceptable QoE levels through adaptive frame selection and dropping strategies.
3Reliability
If frames are selectively dropped to maintain QoE, then video quality is preserved, but network performance rate change must be carefully controlled
Solution Approach 1:
The patent calculates gradient values and frame importance metrics in advance before transmission decisions are made. By pre-computing these parameters and organizing frames according to their characteristics, the system simplifies real-time transmission decisions during congestion events, reducing the complexity of queue management while maintaining effective QoE preservation through predetermined frame selection and dropping rules.
Data Source
AI summary
Methods and systems for a multimedia queue management solution that maintaining graceful Quality of Experience (QoE) degradation are provided. The method selects a frame from all weighted queues based on a gradient function indicating a network performance rate change and a distortion rate caused by the frame and its related frames in the queue, and dropping the selected frame and all its related frames, and continues to drop similarly chosen frame until a network performance rate change caused by the dropping frame and its related frames meets a predetermined performance metric. A frame gradient is a distortion rate divided by a network performance rate change caused by the frame and its related frames, and a distortion rate is based on a sum of each individual frame distortion rate when the frame and its related frames are replaced by some other frames derived from remaining frames based on a replacement method.