Adaptive Bit Rate Encoding with Constrained Fidelity Multicast
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Solution Overview
Problem
Adaptive Bit Rate (ABR) streaming technologies face challenges in managing bandwidth efficiently and ensuring a consistent quality of experience due to client devices requesting maximum bit rates, leading to uneven and unpredictable video quality, especially over unmanaged networks and legacy multicast systems.
Innovation Solution
The implementation of an Agile Streaming Server that regulates content distribution based on bandwidth availability and video quality, using a Constrained Fidelity Constant Bit Rate (CF-CBR) multicast protocol to optimize bandwidth usage and manage video quality across multiple devices and networks, allowing for seamless delivery over both HTTP and multicast protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If client devices request maximum bit rates, then video quality for individual devices is improved, but bandwidth efficiency and network congestion worsen
Solution Approach 1:
The system dynamically changes the bit rate parameter of video content based on network conditions and device capabilities. The ABR streaming protocol adjusts video quality parameters (resolution, bitrate, frame rate) in real-time to optimize the balance between perceived video quality and bandwidth consumption, preventing network congestion while maintaining acceptable quality for each user.
Solution Approach 2:
The streaming system implements dynamic adaptation where video quality parameters are not fixed but continuously adjusted based on real-time network conditions, device capabilities, and user behavior. This dynamic approach allows the system to respond to changing bandwidth availability and prevent congestion while maintaining optimal quality for each user's specific context.
2Adaptability or versatility
If ABR streaming is used to deliver video segments, then adaptability to different devices is improved, but video quality consistency worsens
Solution Approach 1:
The system applies local quality adaptation by tailoring video segments to specific device characteristics and network conditions. Different users receive different quality levels appropriate to their local context (device capabilities, bandwidth, screen resolution), rather than forcing uniform quality across all users. This resolves the contradiction by making quality consistent with each user's local constraints while maintaining overall system adaptability.
Solution Approach 2:
The video content is divided into segments that can be independently encoded at different quality levels. This segmentation allows the system to deliver appropriately quality-adjusted segments to different devices based on their capabilities, maintaining adaptability while managing quality consistency through standardized segment structures and encoding parameters.
3Ease of operation
If sequential HTTP progressive downloads are used, then ease of operation over Internet is improved, but productivity and delivery efficiency worsen
Solution Approach 1:
The system performs preliminary actions by pre-segmenting video content into standardized chunks and pre-establishing delivery pathways before actual playback. This allows for more efficient sequential downloads compared to traditional progressive downloading, as the segments are pre-prepared and can be delivered in an optimized sequence, improving delivery efficiency while maintaining HTTP protocol simplicity and ease of operation.
Data Source
AI summary
This disclosure describes and adaptive bit rate encoding and distribution techniques for conserving bandwidth usage in a channel. The invention comprises, an encoder or transcoder, a video fragmenter, a video-quality analyzer that output complexity values, a streaming server, a process by which individual fragments are selected for distribution, a video-quality threshold, and, optionally a bandwidth reclamation factor. A video-quality analyzer inspects any combination of the input and output of the encoder, transcoder, or fragmenter, and produces a video-quality metric for each fragment. A fragment-selection process responds to request from a client device. If the video-quality value of the fragment requested exceeds the video-quality threshold, a different fragment having a lower vide-quality value is selected instead. Otherwise, the fragment that would have been selected is selected. In some embodiments, the video-quality threshold can be dynamically adjusted to permit varying amounts of bandwidth reclamation.