Streaming Quality Metrics Classes for Consistent Reporting
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Solution Overview
Problem
Current streaming quality metrics reporting in PSS systems is imprecise due to ambiguities in defining a 'good frame' and different error tracking algorithms used by various terminals, leading to inconsistent and worthless quality reports across terminals and content types.
Innovation Solution
A method for improved streaming quality reporting that selects at least one quality metric and a quality metrics class from a pre-defined set, allowing negotiation between the client and server using RTSP and SDP to determine the quality of streaming based on selected metrics, with additional quality metrics classes providing fixed definitions for determining good frames, thus enhancing the precision and conciseness of quality reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different error tracking algorithms are used by various terminals to determine quality metrics, then terminals can independently evaluate streaming quality, but the quality reports become inconsistent and worthless across different terminals
Solution Approach 1:
The patent introduces a quality metrics class parameter that defines standardized algorithms for determining good frames and quality metrics. By changing the parameter space to include predefined classes (e.g., Class 0, Class 1, Class 2) with specific algorithmic definitions, the patent ensures that all terminals use consistent methods for quality evaluation while maintaining adaptability through negotiation between client and server.
Solution Approach 2:
The patent segments the quality evaluation process into distinct, predefined classes with specific algorithms. Each quality metrics class represents a segmented approach to error tracking and good frame determination, allowing terminals to select from standardized options rather than implementing arbitrary algorithms, thereby ensuring consistency across different terminals.
2Measurement precision
If quality metrics definitions are made more specific with additional quality metrics classes, then measurement precision improves, but the complexity of the system increases
Solution Approach 1:
The patent divides the quality metrics system into segmented, predefined classes (Class 0, Class 1, Class 2, etc.), where each class has a specific algorithmic definition. This segmentation provides precision through clear definitions while managing complexity by organizing the system into discrete, manageable units that can be negotiated and selected rather than implemented in full detail at each terminal.
Solution Approach 2:
The patent creates a universal framework where a small set of quality metrics classes serves multiple functions: they define good frame determination, establish error tracking algorithms, and enable negotiation between client and server. This multi-functionality achieves precision without proportionally increasing complexity, as the same class definitions serve multiple purposes in the streaming system.
3Device complexity
If ambiguous definitions of 'good frame' are used in quality metrics, then the system remains simple and flexible, but the quality reports lose significance and become worthless
Solution Approach 1:
The patent changes the parameter of frame quality definition from ambiguous to precise by introducing quality metrics classes with explicit algorithmic definitions. Each class specifies exactly how to determine if a frame is 'good' (e.g., based on error-free reception, reference frame availability, or other objective criteria), thereby ensuring reliability while maintaining system simplicity through standardized parameters.
Solution Approach 2:
The patent enables the streaming system to self-define its quality metrics through the negotiation of quality metrics classes between client and server. Rather than requiring external specification or complex manual configuration, the system automatically agrees upon the definitions to be used, maintaining simplicity while ensuring reliability through mutual agreement on precise criteria.
Data Source
AI summary
A method for reporting a streaming quality is shown, wherein at least one continuous media stream is streamed to a client (601), and wherein said streaming is controlled by a protocol (109) that is operated between said client (601) and a server (600), the method including selecting at least one quality metric and a quality metrics class from a pre-defined set of at least two quality metrics classes, and reporting to said server (600) the quality of said streaming based on said at least one selected quality metric and said selected quality metrics class. The protocol (109) is preferably a Real-time Streaming Protocol in combination with a Session Description Protocol in the context of the 3GPP Packet-Switched Streaming Service. Also shown is a computer program, a computer program product, a system, a client, a server and a protocol.