Video Streaming Bit-Rate Adjustment via Frame Complexity Analysis
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Solution Overview
Problem
Current video streaming methods consume excessive data, especially when streaming HD or full-HD videos, as they do not effectively differentiate between frame complexity and motion, leading to inefficient bit-rate adjustments based on network conditions alone, resulting in either buffering or degraded user experience.
Innovation Solution
Analyzing the complexity of video frames based on texture and motion to adjust the bit-rate of subsequent fragments, ensuring no perceivable quality difference between streamed fragments while optimizing data usage, by determining scene content complexity using indices like MSSIM and SSIM, and adjusting bit-rates accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video is streamed at higher bit-rates (HD or full-HD), then visual quality is improved, but data consumption increases significantly
Solution Approach 1:
The patent applies local quality by analyzing frame complexity metrics (motion, texture, edge density) for different portions of the video stream and adjusting bit-rate allocation accordingly. High complexity frames receive higher bit-rates while low complexity frames receive lower bit-rates, optimizing the balance between visual quality and data consumption on a frame-by-frame basis rather than uniformly across the entire video.
Solution Approach 2:
The system dynamically adjusts bit-rate in real-time based on continuously calculated frame complexity metrics. The bit-rate adaptation responds to changing video content characteristics, network conditions, and device capabilities, transitioning between different quality levels seamlessly to maintain optimal visual experience while minimizing data usage.
2Reliability
If ABR policy switches quality based on network conditions, then buffering is avoided, but data saving is limited due to lack of frame complexity consideration
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring frame complexity metrics (motion magnitude, texture variability, edge density) and using this information to adjust bit-rate decisions. The system feeds back the analyzed complexity information to the bit-rate selection logic, enabling intelligent adaptation that goes beyond simple network condition responses to achieve both streaming reliability and data efficiency.
Solution Approach 2:
The system changes the parameter of bit-rate based on multiple inputs including network conditions and frame complexity metrics. By modifying the bit-rate parameter dynamically according to the calculated complexity values, the system achieves more efficient data utilization while maintaining streaming continuity, selecting from multiple encoded versions at different quality levels.
3Quantity of substance
If data saving method streams at lowest quality, then maximum data saving is achieved, but user experience is degraded
Solution Approach 1:
The patent applies local quality by differentiating between high complexity frames (requiring higher bit-rates for acceptable quality) and low complexity frames (where lower bit-rates suffice). This selective quality allocation ensures that data is consumed primarily when visually necessary, maintaining user experience for important visual content while achieving significant data savings during low complexity segments.
Solution Approach 2:
The system dynamically changes the quality parameter based on frame complexity analysis, transitioning between different quality levels rather than maintaining a fixed low quality. This enables the system to preserve user experience when needed (high complexity frames) while maximizing data savings when possible (low complexity frames).
Data Source
AI summary
Embodiments herein provide methods and systems for saving data while streaming a video. The embodiments include streaming a first fragment of the video at a bit-rate based on network conditions. In an embodiment, the scene content complexity of the first fragment can be analyzed while streaming the first fragment. Based on the screen complexity, the second fragment can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. In another embodiment, a second fragment can be received at a minimum possible resolution and, thereafter, the scene content complexity of the second fragment can be analyzed while streaming the first fragment. Based on the screen complexity of the second fragment, it can be streamed at a bit-rate based on the network conditions and the analyzed screen complexity. There may be no perceptible difference in streamed quality of the first fragment and the second fragment.