Encoding Ladder Optimization for QoE and Storage Tradeoffs
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Solution Overview
Problem
Existing video streaming technologies generate encoding ladders that are sub-optimal in terms of streaming quality of experience (QoE) and storage footprint due to sequential enforcement of ladder constraints, missing opportunities to select encoded videos that satisfy multiple constraints simultaneously.
Innovation Solution
A computer-implemented method using constrained optimization techniques to generate encoding ladders by formulating a parameterized objective function that balances QoE and storage footprint, concurrently accounting for multiple ladder constraints, and employing a simulation evaluation to identify optimal encoding ladders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If ladder constraints are enforced sequentially using heuristics, then the encoding ladder can be constructed incrementally, but the streaming QoE/storage footprint tradeoff becomes sub-optimal
Solution Approach 1:
The patent transforms the encoding ladder generation problem into a parameterized optimization problem where ladder constraints are expressed as mathematical parameters. By changing the approach from sequential heuristic enforcement to concurrent optimization with parameterized constraints, the system achieves optimal streaming QoE/storage footprint tradeoff while maintaining ease of construction through automated optimization algorithms.
Solution Approach 2:
The patent introduces a new dimension of optimization by formulating the problem in terms of objective functions and constraints that can be concurrently evaluated. This dimensional transformation allows the system to consider multiple ladder constraints simultaneously in a unified optimization framework, rather than processing them sequentially in the original one-dimensional heuristic approach.
2Reliability
If multiple encoded videos are added to satisfy different ladder constraints, then streaming QoE requirements are met, but storage footprint increases unnecessarily
Solution Approach 1:
The patent merges multiple ladder constraints into a unified optimization framework where a single encoded video can satisfy multiple constraints simultaneously. By combining the evaluation of different ladder constraints into concurrent optimization, the system identifies encoded videos that meet multiple requirements at once, reducing the total number of videos needed and thereby decreasing storage footprint while maintaining streaming QoE.
Solution Approach 2:
The patent makes the encoding ladder generation process multi-functional by designing an optimization system that simultaneously evaluates multiple ladder constraints and optimizes both streaming QoE and storage footprint together. This universal approach allows a single optimization process to achieve multiple objectives concurrently, rather than requiring separate sequential processes for each constraint.
3Illumination intensity
If the encoding ladder includes more encoded videos to improve visual quality, then streaming QoE increases, but CDN storage resources are wasted
Solution Approach 1:
The patent changes the approach from adding more encoded videos to improving visual quality to optimizing the selection of encoded videos through parameterized optimization. By formulating visual quality and storage footprint as parameters in an objective function, the system identifies the minimal set of encoded videos needed to achieve target visual quality levels, thereby preventing waste of CDN storage resources.
Solution Approach 2:
The patent creates a virtual model or copy of the encoding ladder generation process through parameterized optimization algorithms. This computational model allows the system to evaluate and compare different encoding ladder configurations without actually storing multiple redundant sets of encoded videos, enabling optimal resource utilization while maintaining the ability to assess visual quality across different configurations.
Data Source
AI summary
In various embodiments, an encoding ladder application generates encoding ladders that are used to stream media titles. The encoding ladder application generates an objective function based on a ladder configuration and a parameterized objective function. The parameterized objective function approximates a tradeoff between a quality of experience and a cost term associated with a candidate encoding ladder. The encoding ladder application generates constraints based on the ladder configuration and parameterized constraints. The encoding ladder application executes a constrained optimization algorithm on the objective function, the constraints, and encoding point metadata associated with a set of encoded videos to generate a first candidate encoding ladder for a media title.