Adaptive Video Streaming Optimizer for Burst Management
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Solution Overview
Problem
Existing adaptive video streaming systems often result in a poor user experience due to low video resolution and frequent resolution changes caused by network bursts, which degrade the viewing experience and are not effectively managed by current technologies.
Innovation Solution
An adaptive video streaming system that includes an optimizer to predict and manage network bursts, ensuring maximal bit-rate playback while minimizing player resolution changes by intercepting manifest requests, shaping network traffic, and proactively downloading fragments to maintain high-quality video playback with minimal resolution changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the player measures downloading data rate to decide about resolution change, then the system adapts to network conditions, but frequent resolution changes occur due to network bursts
Solution Approach 1:
The system proactively predicts future network conditions and pre-loads video fragments at appropriate resolutions before network bursts occur. By anticipating network state changes and preparing buffered content in advance, the player avoids frequent resolution switches while maintaining adaptability to actual network conditions.
Solution Approach 2:
The system creates a buffer of pre-loaded video fragments that cushions against network variability. This buffer absorbs the impact of network bursts, allowing the player to maintain stable resolution playback without immediately reacting to temporary network fluctuations, thus reducing frequent resolution changes.
2Manufacturing precision
If the player gathers a buffer of data to solve low resolution issues, then video quality improves, but download time increases
Solution Approach 1:
The system performs preliminary analysis of network conditions and prediction of future states to determine optimal buffering strategies in advance. By predicting when network conditions will improve, the system pre-loads fragments at higher resolutions during favorable conditions, achieving high video quality without excessive download time.
Solution Approach 2:
The system dynamically adjusts buffering parameters such as buffer size, fragment selection, and resolution levels based on predicted network conditions. This adaptive parameter adjustment allows the system to optimize the balance between video quality and download time by loading appropriate amounts of data at appropriate resolutions based on forecasted network performance.
Data Source
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AI summary
An adaptive video streaming system comprises a computer network (14) comprising a data source, a video player (120), a session controller (130) configured to use in parallel a variable number of streams in order to maximize download throughput from said data source to said video player (120), and an adaptive streaming optimizer (210) connected between said data source and said video player (120), the adaptive streaming optimizer (210) configured to predict the next resolution to be requested by the player (120) and determine which fragments to download accordingly.