Adaptive Video Encoding via Cloud Virtualization
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Solution Overview
Problem
Conventional video encoding systems are expensive, require significant investment in hardware and maintenance, and are designed for peak utilization, leading to inefficiencies and high costs due to the need for dedicated server farms and continuous updates.
Innovation Solution
The system dynamically instantiates and disconnects virtual encoding devices based on viewer counts, using cloud-based encoders to adjust bitrates and resolutions in real-time, ensuring efficient resource usage and cost savings by scaling encoding capacity according to demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated server farms and continuous hardware updates are used to ensure reliable video transmission, then transmission reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses virtualization to create virtual copies of encoding resources instead of relying on physical dedicated servers. Virtual encoding environments can be instantiated and replicated as needed, providing reliable transmission capabilities without the complexity of maintaining physical server farms. The virtual encoding environment abstracts the underlying hardware complexity while maintaining service reliability.
Solution Approach 2:
The patent replaces the mechanical/physical system of dedicated server farms with a software-based virtual encoding environment. This substitution eliminates the need for continuous hardware updates and physical infrastructure maintenance while maintaining transmission reliability through virtualized resource management and orchestration.
2Reliability
If encoding capacity is designed for peak utilization, then transmission reliability during high demand is improved, but loss of energy increases during low-demand periods
Solution Approach 1:
The patent implements dynamic encoding capacity adjustment by scaling the number of virtual encoding instances based on real-time viewer count and demand. During peak utilization, additional virtual encoders are instantiated to maintain transmission reliability. During low-demand periods, encoding capacity is reduced or paused, eliminating energy waste while ensuring reliability is maintained when needed.
Solution Approach 2:
The system dynamically changes operational parameters such as the number of active encoding instances, bitrate, and resolution based on viewer count and network conditions. This allows the system to maintain transmission reliability during high demand while minimizing energy consumption during low-demand periods by adjusting parameters rather than maintaining fixed peak-capacity infrastructure.
3Productivity
If the number of encoders is increased to handle viewer demand, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent dynamically adjusts encoding capacity by instantiating or deactivating virtual encoder instances based on viewer count thresholds. When viewer count increases above a threshold, additional encoders are activated to maintain productivity. When viewer count drops below a threshold, encoders are deactivated to reduce energy consumption, achieving dynamic optimization of the productivity-energy tradeoff.
Solution Approach 2:
The virtual encoding environment provides multi-functionality by serving multiple purposes: it can scale encoding capacity up or down as needed, provide load balancing, ensure transmission reliability, and optimize energy usage. This universal approach replaces the need for dedicated hardware encoders for each function, reducing overall energy consumption while maintaining productivity.
4Adaptability or versatility
If cloud-based virtual encoders are used to scale encoding capacity, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary encoding management system that orchestrates virtual encoder instantiation, routing, and deactivation. This intermediary layer provides adaptability by dynamically adjusting encoding capacity based on viewer count while abstracting the complexity of cloud-based virtualization from the core streaming functionality. The management system handles the complexity of coordinating multiple virtual encoders, presenting a simplified interface to the rest of the system.
Data Source
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AI summary
The disclosed technology relates to adaptively encoding a video stream based on viewer engagement, content schedule and/or content characteristics. A system routes a video stream from a signal extractor to a first virtual encoder. The first virtual encoder is instantiated on a cloud platform and provides a first video output at a first bitrate. A second virtual encoder is instantiated on the cloud platform in response to a change in viewer count. The system routes the video stream from the signal extractor to the second virtual encoder. The second virtual encoder provides a second video output at a second bitrate. The second bitrate is different from the first bitrate.