Streaming Media Quality Assessment via Packet Distortion Scoring
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Solution Overview
Problem
Existing network performance metrics fail to accurately capture the impact of transmission errors on user experience in streaming media, particularly due to their content dependency and inability to account for error correction and concealment strategies, leading to a disconnect between network performance and user quality perception.
Innovation Solution
A content-independent evaluation method that calculates a distortion score by assigning packets to time windows, determining local disturbance scores based on transmission performance metrics, and aggregating these scores to assess perceived quality, accounting for packet loss patterns and error correction mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network performance metrics are used to evaluate streaming media quality, then measurement is simpler, but accuracy in capturing user experience degradation is insufficient
Solution Approach 1:
The patent segments the streaming media transmission into discrete packets and organizes them into time windows. Each packet is individually evaluated for transmission performance metrics (loss, delay, jitter), and local disturbance scores are calculated for each time window. This segmentation enables precise measurement of transmission degradation at granular levels while maintaining manageable computational complexity through structured processing.
Solution Approach 2:
The patent introduces transmission performance metrics and local disturbance scores as intermediary variables between raw packet data and final quality assessment. These intermediaries translate complex network behaviors (packet loss patterns, timing variations) into standardized scores that can be aggregated into distortion scores, bridging the gap between network-level measurements and user-perceived quality without requiring direct content analysis.
2Measurement precision
If content-dependent quality assessment methods are used, then accuracy in evaluating user experience is improved, but adaptability to different content types and error correction strategies is reduced
Solution Approach 1:
Instead of analyzing media content directly to assess quality (content-dependent approach), the patent inverts the approach by analyzing transmission performance metrics of packets (content-independent approach). It evaluates how packets were transmitted (loss patterns, timing) rather than what the content is, making the assessment universally applicable to different content types and error correction strategies while still accurately reflecting user experience degradation.
Solution Approach 2:
The patent creates a universal evaluation framework that works across different streaming content types (video, audio, interactive media) and different error correction strategies (FEC, retransmission, concealment). By focusing on transmission performance metrics rather than content-specific analysis, the distortion score methodology provides a multi-functional assessment tool that adapts to various应用场景 without requiring content-specific calibration.
3Measurement precision
If detailed packet-level analysis is performed to capture transmission errors, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments packet analysis into time-windowed batches rather than processing all packets individually in sequence. By organizing packets into time windows and calculating local disturbance scores for each window, the system achieves detailed packet-level measurement precision while enabling parallel processing and reducing overall computation time compared to sequential analysis of every packet.
Data Source
AI summary
In one embodiment, a device in a network assigns packets from a communication transmitted via the network to time windows over a period of time. The device determines a transmission performance metric for each of the packets in a particular time window and calculates, for each of the time windows, local disturbance scores, which are based on the transmission performance metrics for the packet in the time windows. A particular local disturbance score for a particular time window maps the transmission performance metrics for the packets in the time window to a perceived quality metric. The device determines a distortion score for the communication by aggregating the local disturbance scores for the time windows over the period of time.