Video Streaming Engagement Estimation via Quality Parameter Equations
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Solution Overview
Problem
Current technologies lack a method to effectively estimate engagement with video streaming, such as viewing time and acceptance, which is crucial for video streaming providers to ensure high-quality user experience.
Innovation Solution
An engagement estimation apparatus that acquires quality parameter values, like resolution, frame rate, and playback times, and uses these to calculate an engagement index through equations derived from subjective evaluations, allowing for real-time monitoring and improvement of video streaming services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If quality parameters (resolution, frame rate, bit rate) are reduced to adapt to network conditions, then network adaptability is improved, but video quality deteriorates
Solution Approach 1:
The patent implements dynamic quality adjustment by continuously monitoring network conditions (throughput, packet loss, delay) and adapting video parameters (resolution, frame rate, bit rate) in real-time. The system transitions from static quality settings to dynamic adaptation, allowing the video streaming service to optimize between network adaptability and video quality based on current conditions.
Solution Approach 2:
The patent changes multiple video parameters simultaneously (resolution, frame rate, bit rate) to adapt to network conditions. By adjusting these parameters dynamically, the system resolves the contradiction between maintaining high video quality and adapting to varying network capabilities, as each parameter can be tuned independently based on network state.
2Stability of the object's composition
If buffer processing is implemented to handle network fluctuations, then playback stability is improved, but playback start waiting time increases
Solution Approach 1:
The patent implements preliminary buffer filling before playback starts, accumulating sufficient data in advance to prevent playback interruptions. By pre-loading content into the buffer based on predicted network conditions, the system ensures stable playback without excessive waiting time, as the buffer is prepared optimally before playback begins.
Solution Approach 2:
The patent uses partial buffering strategies where the buffer size and filling rate are adjusted dynamically. Instead of always filling the buffer to maximum capacity, the system uses just enough buffering to maintain stability, reducing unnecessary waiting time while still preventing playback interruptions during normal operation.
3Measurement precision
If existing quality estimation techniques are used, then quality evaluation is enabled, but engagement estimation is not available
Solution Approach 1:
The patent extends the functionality of quality estimation techniques to include both quality evaluation and engagement estimation. By using the same quality parameters (resolution, frame rate, bit rate, playback stability) for dual purposes, the system achieves multi-functionality, extracting both quality metrics and engagement indicators from the same data sources without requiring separate measurement systems.
Solution Approach 2:
The patent introduces quality parameters as intermediary variables that link network conditions to both quality perception and engagement. These parameters serve as mediators that translate technical metrics into meaningful insights about both video quality and user engagement, allowing the system to estimate engagement indirectly through quality-related measurements.
Data Source
AI summary
An engagement estimation apparatus includes: an acquisition unit configured to acquire a value of a parameter affecting a quality of video streaming in a certain period; and a calculation unit configured to substitute the value of the parameter acquired by the acquisition unit in an equation representing a relation between the parameter indicative of the quality and engagement with the video streaming to calculate a value of an index indicative of the engagement, thereby enabling estimation of the engagement with the video streaming.