Streaming Content Load Tuning for Context-Specific Supplemental Delivery
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Solution Overview
Problem
Conventional systems fail to optimize supplemental content load parameters efficiently and scalably, neglecting viewer and provider load tolerances, leading to inferior performance metrics due to cumbersome and resource-intensive experimental processes.
Innovation Solution
A computing system uses slice configurations to set load parameters for streaming contexts, monitoring performance metrics, and iteratively optimizing load tuning through machine learning, enabling efficient and scalable load parameter adjustments across diverse streaming scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use hard-coded logic at multiple servers/services to test load parameter changes, then experimental control is possible, but system complexity and resource consumption increase significantly
Solution Approach 1:
The patent introduces a load tuning controller as an intermediary component that centralizes the management of load parameters. This controller receives slice configurations from a user interface, identifies matching streaming instances, and applies the appropriate load parameters without requiring modifications to hard-coded logic at multiple servers. The intermediary abstracts the complexity, allowing precise load parameter optimization while maintaining system simplicity.
2Measurement precision
If conventional systems manually modify hard-coded logic at multiple servers for each parameter change, then load parameter testing can be performed, but productivity and efficiency decrease
Solution Approach 1:
The load tuning controller automatically performs the entire load parameter testing process without requiring manual intervention at multiple servers. It self-services by receiving slice configurations, automatically identifying matching streaming instances, applying load parameters, and monitoring performance metrics. This automation eliminates the need for manual hard-coded logic modifications, significantly improving productivity while maintaining measurement precision.
3Ease of manufacture
If conventional systems apply uniform load parameters across all streaming instances, then implementation is simple, but adaptability to different viewer cohorts and contexts is lost
Solution Approach 1:
The patent implements local quality by allowing different load parameters to be applied to different streaming instances based on their specific contexts. The load tuning controller receives slice configurations that define particular streaming contexts (e.g., viewer cohort, device type, content category) and applies customized load parameters only to matching instances. This enables context-specific optimization while maintaining ease of implementation through the centralized controller interface.
4Measurement precision
If conventional systems run individualized experiments for different viewer cohorts, then granular optimization is achieved, but time and resource costs become prohibitively expensive
Solution Approach 1:
The patent merges multiple individualized experiments into a single unified experimental framework. The load tuning controller simultaneously manages load parameter testing across multiple viewer cohorts and streaming contexts by processing slice configurations in a centralized manner. This consolidation maintains cohort-specific measurement precision while dramatically reducing the time and resources required compared to running separate experiments for each cohort.
Data Source
AI summary
A method of applying load configurations, for use in presenting supplemental content with streaming content, includes obtaining a slice configuration specifying (i) values of one or more slice parameters that define a particular streaming context and (ii) values of one or more load parameters that control supplemental content loads for streaming recipients. The method also includes identifying, from among a plurality of streaming instances provided by a streaming platform, streaming instances that match the particular streaming context, setting, for each of the identified streaming instances, the one or more load parameters to the specified values of the one or more load parameters, and determining one or more performance metrics by monitoring events associated with the identified streaming instances.