Media Compression Optimization via Dynamic Resource Allocation
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Solution Overview
Problem
Current data communication networks face challenges in optimizing compression tasks to maximize throughput reduction while keeping costs within available resources, especially in managing processing resources and caching storage for media streaming operations.
Innovation Solution
A method and system that identify available resources, estimate throughput reduction and cost of different compression tasks for media items, and determine an optimization solution to maximize throughput reduction while keeping costs within limits, using a processor to select and apply appropriate compression tasks based on current and future gains, and manage look-up tables for updating gain values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If sophisticated compression methods are used to reduce bandwidth utilization, then throughput reduction is improved, but processing power requirements increase
Solution Approach 1:
The system dynamically adjusts compression parameters and selection based on available processing resources and network conditions. The optimization algorithm modifies compression task parameters to achieve the best throughput reduction within the constraints of available processing power, rather than always using maximum compression strength.
Solution Approach 2:
The compression task selection is made dynamic by continuously monitoring available processing resources and adjusting the compression strategy in real-time. The system transitions from static compression methods to dynamic selection based on current system state, allowing adaptation between compression strength and processing capacity.
2Loss of energy
If compression tasks are applied to maximize throughput reduction, then bandwidth utilization is improved, but available processing resources are consumed
Solution Approach 1:
Instead of applying maximum compression to all media items, the system applies partial compression only to the most beneficial items. The optimization algorithm identifies which media items yield the highest throughput reduction per unit of processing resource consumed, and applies compression selectively to those items rather than uniformly across all traffic.
Solution Approach 2:
Different compression strategies are applied to different media items based on their individual characteristics and the expected throughput reduction. The system identifies local optima for each media item by evaluating compression gain versus processing cost, and applies appropriate compression tasks only where the local benefit justifies the resource consumption.
3Productivity
If multiple compression tasks are evaluated and selected, then throughput optimization is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary evaluation and ranking of compression tasks for different media items before actual compression occurs. By pre-calculating the expected throughput reduction and processing cost for various compression options, the system simplifies the real-time decision-making process and avoids the need to evaluate all compression combinations during active transmission.
Data Source
AI summary
A method for handling communication data involving identifying available resources for applying compression tasks and estimating a throughput reduction value to be achieved by applying each of a plurality of different compression tasks to a plurality of media items. A cost of applying the plurality of different compression tasks to the plurality of media items is estimated. The method further includes finding an optimization solution that maximizes the throughput reduction value over possible pairs of the compression tasks and the media items, while keeping the cost of the tasks of the solution within the identified available resources and providing instructions to apply compression tasks according to the optimization solution.