Building a 300 Channel Video Encoding Server

Problem Statement

The demand for high-quality live video streaming has surged in recent years, putting pressure on operational costs and user expectations. Legacy x86 processors struggle to handle the intensive video processing tasks required for modern streaming. This article explores how to build a scalable video encoding server that can support 300 channels of live video streaming.

System Requirements

To achieve real-time video encoding at scale, we need a system that can:

• Handle multiple video streams simultaneously
• Perform high-bandwidth video compression and encoding
• Provide low-latency processing for real-time streaming
• Be scalable to meet increasing demand

Our solution is based on the following architecture:

Hardware Components

• Server: Supermicro TwinPro servers with Ampere Computing’s Altra processor
• Storage: NVMe-based storage arrays for high-speed data access
• Networking: 100GbE networking infrastructure for low-latency communication between components

Software Components

• Video Encoding Engine: NETINT’s x86-64 video encoding engine, optimized for Altra processors
• Operating System: Linux-based operating system with kernel-level support for hardware acceleration

Video Encoding Engine Implementation

Our video encoding engine is built on top of NETINT’s software stack, which provides a highly optimized and scalable implementation of the H.264/AVC and H.265/HEVC codecs.

Here’s an excerpt from our code:

#include <netint/video_encode.h>

// Initialize video encode context
void init_video_encode_context() {
// Allocate memory for encoding parameters
netint_encode_param_t* param = malloc(sizeof(netint_encode_param_t));

// Set encoding settings (e.g., resolution, framerate)
param->width = 1920;
param->height = 1080;
param->fps = 30;

// Initialize encode context with parameters
netint_init_encode_context(param);
}

// Encode a frame of video data
void encode_frame(void* frame_data) {
// Perform encoding using the initialized context
netint_encode_frame(frame_data);
}

Scalability and Performance

To achieve high scalability, we implemented a distributed architecture where multiple servers work together to handle video encoding tasks. Each server is responsible for encoding a subset of channels, and they communicate with each other over 100GbE networking infrastructure.

Our implementation includes several performance optimization techniques:

• Multi-threading: We use multi-threading to parallelize video encoding tasks on each CPU core
• Scheduling: We implement a scheduling algorithm that dynamically assigns encoding tasks to available servers based on their load and capacity

Here’s an excerpt from our code:

// Create a thread pool for encoding tasks
pthread_t thread_pool[THREAD_POOL_SIZE];

// Assign encoding task to a worker thread
void assign_task(pthread_t* thread, void* frame_data) {
// Submit task to worker thread using a queue data structure
enqueue_frame(thread, frame_data);
}

// Worker function that performs video encoding
void* worker_thread(void* arg) {
// Dequeue tasks from the queue and perform encoding
while (true) {
void* frame_data = dequeue_frame();
encode_frame(frame_data);
}
}

Conclusion

Building a 300 channel video encoding server requires careful consideration of system architecture, hardware components, software implementation, and performance optimization techniques. By leveraging cutting-edge technologies like Ampere Computing’s Altra processor and NETINT’s optimized video encoding engine, we can achieve real-time video encoding at scale while meeting the demands of modern live streaming applications.

Future Work

To further improve our solution, we plan to explore:

• Cloud-native deployment: We aim to deploy our system on cloud platforms like AWS or Google Cloud to take advantage of their scalability and manageability features
• Artificial intelligence (AI) enhancements: We’ll investigate integrating AI-powered video processing techniques, such as object detection and tracking, to enhance the streaming experience

Example Use Cases

Our scalable video encoding server is ideal for various use cases:

• Live sports broadcasting: Our system can handle high-bandwidth video streams with low latency, making it perfect for live sports broadcasting
• Virtual events: We can support virtual events by providing real-time video conferencing and streaming capabilities
• Social media platforms: Our scalable architecture can handle massive user traffic and provide on-demand video streaming services

Advice for Developers

If you’re building a similar system, keep the following in mind:

• Scalability is key: Ensure your system can scale horizontally to meet increasing demand
• Optimize performance: Implement techniques like multi-threading, scheduling, and caching to improve encoding performance
• Cloud-native deployment: Consider deploying on cloud platforms for scalability and manageability

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By Malik Abualzait