Model-Driven Mobile Content Adaptation for QoE Consistency
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Solution Overview
Problem
Existing multimedia content delivery systems face challenges in providing consistent Quality of Experience (QoE) across heterogeneous environments due to limited context information and vendor-specific adaptations, leading to sub-optimal decision-making and varying user experiences.
Innovation Solution
A model-driven approach that utilizes multiple contexts, such as device, user, and location information, to create an abstract adaptation model which is then transformed into an implementable model, allowing for flexible deployment and real-time adaptation decisions across various platforms and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vendor-specific adaptation techniques are used with limited context information, then implementation complexity is reduced, but Quality of Experience consistency deteriorates
Solution Approach 1:
The patent creates a universal adaptation model that works across multiple platforms and deployment environments (mobile, web, enterprise). The model uses a standardized set of context parameters and quality of experience impactors that can be applied universally, eliminating the need for vendor-specific implementations while maintaining QoE consistency across heterogeneous systems
Solution Approach 2:
The patent transforms the adaptation approach by changing from limited context parameters (bandwidth, buffering rate only) to a comprehensive set of context parameters including device context, user context, location context, and network context. This parameter expansion enables more accurate adaptation decisions that maintain QoE consistency without excessive complexity
2Ease of operation
If only available bandwidth is used as context for adaptation, then decision-making simplicity is improved, but adaptation optimality deteriorates
Solution Approach 1:
The patent segments the context information into distinct domains: device context (screen size, resolution, capabilities), user context (preferences, historical behavior), location context (GPS coordinates, environmental factors), and network context (bandwidth, latency, packet loss). This segmentation allows comprehensive adaptation while maintaining manageable complexity through organized context categories
Solution Approach 2:
The patent introduces quality of experience impactors as intermediary elements that connect context parameters to adaptation decisions. These impactors (buffering rate, start-up time, playback smoothness, quality level) serve as mediators that translate multiple context factors into actionable adaptation rules, improving optimality without overwhelming decision-making complexity
3Adaptability or versatility
If abstract adaptation model is created with multiple contexts, then adaptation flexibility is improved, but model complexity increases
Solution Approach 1:
The patent performs preliminary action by creating an abstract adaptation model that defines all possible context parameters, quality of experience impactors, and adaptation rules in advance. This pre-defined framework allows flexible adaptation to various platforms and scenarios without increasing runtime complexity, as the model structure is established beforehand and can be instantiated across different deployment environments
Data Source
AI summary
The invention relates to a system and method for adapting mobile multimedia content delivery service to enhance the quality of experience of one or more users. This invention involves identifying all the contexts from different domains associated with the mobile multimedia content delivery service that can impact on the quality of experience of the end user. The invention maps the contexts with the quality of experience impactors. Based on this information an abstract adaptation model is created to define basic rules of adaptation. This model also defines the threshold for adaptation and also the different adaptation actions corresponding to different contexts. This model can be transformed into an implementable adaptation model taking real time constraints into consideration. The available contexts in real time are mapped with the contexts present in the abstract model and then decision making module decides when to adapt and how to adapt the multimedia content.