Video Delivery Experience Optimizer for Incremental Pre-loading
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Solution Overview
Problem
Existing online video streaming systems waste resources by pre-loading videos that users may not access, as they rely on non-customized protocols for recommending videos, leading to inefficiencies in video delivery and user experience.
Innovation Solution
Implementing an experience optimizer that learns real-time user access probabilities to create an ordered prediction set of videos, allowing incremental preparation and delivery of videos based on these probabilities, using spare resources and reducing waste by canceling irrelevant preparations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If videos are pre-loaded using standard recommendation protocols, then users can access recommended videos, but resources are wasted on videos users may not access
Solution Approach 1:
The system performs preliminary actions by pre-loading videos into the user device before the user actually requests them. The server identifies videos likely to be accessed based on current viewing context and pre-loads them during off-peak times or in the background, so when the user requests a recommended video, it is already available locally or in the buffer, eliminating access delays while only loading videos with high predicted access probability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user viewing behavior, video completion rates, and engagement metrics. This feedback data is used to refine the machine learning models that predict which videos users are most likely to access. The feedback loop ensures the pre-loading algorithm becomes increasingly accurate over time, reducing resource wastage on videos users never watch.
2Speed
If all recommended videos are pre-loaded, then user access latency is reduced, but bandwidth and storage resources are consumed unnecessarily
Solution Approach 1:
The system applies local quality by customizing the pre-loading strategy to each user's specific context and preferences. Rather than uniformly pre-loading all recommended videos, the system identifies and pre-loads only the subset of videos most relevant to that particular user based on their viewing history, demographic profile, and current content consumption patterns. This personalized approach ensures high access speed for relevant videos while minimizing unnecessary bandwidth consumption.
Solution Approach 2:
The system changes key parameters including pre-loading time windows, buffer sizes, and video quality levels based on predicted user behavior patterns. The machine learning models dynamically adjust these parameters to optimize between access speed and resource consumption, pre-loading videos during off-peak hours or using lower quality streams initially, then upgrading to higher quality when the user actually accesses the content.
3Loss of energy
If videos are prepared incrementally based on prediction sets, then resource wastage is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer in the form of a machine learning prediction engine that sits between the user's video requests and the content delivery infrastructure. This intermediary analyzes user behavior patterns, contextual information, and historical data to generate personalized prediction sets, automatically filtering which videos should be pre-loaded. This intermediary layer handles the complexity of prediction and selection, shielding the rest of the system from the intricacies of personalized recommendation algorithms.
Solution Approach 2:
The system segments the video delivery process into distinct phases: prediction phase (where ML models identify likely videos), pre-loading phase (where videos are asynchronously fetched), and delivery phase (where videos are served to users). This segmentation allows each phase to be optimized independently, with the prediction and pre-loading operations running in the background without blocking the main video streaming experience, thereby managing system complexity through modular architecture.
4Productivity
If real-time learning of access probabilities is implemented, then video delivery optimization improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary learning and prediction computations during off-peak hours or in advance, building up prediction models and cached results before actual video requests occur. The machine learning models continuously learn from historical data in the background, so when a user requests a video, the system already has refined predictions about which videos are likely to be accessed, minimizing real-time processing delays while maintaining high delivery optimization.
Data Source
AI summary
Methods and arrangements for video delivery preparation. A video, being accessible online, is displayed at an interface in response to a user request. A relative probability of video access by the user with respect to at least two other online-accessible videos is qualitatively determined. An ordered prediction set is created with respect to the other videos. The other videos are prepared incrementally based on the prediction set, so that the user is able to access and view the videos. The preparing includes preparing a first one of the other videos over a first time period and a second one of the other videos over a second time period, wherein at least a portion of the first time period is coincident with at least a portion of the second time period. An element is displayed at the interface which provides a connection to at least one of the other videos.