Transcoding and adaptive bitrate in HLD - Scalability & System Analysis

Scalability Analysis - Transcoding and adaptive bitrate

Growth Table: Transcoding and Adaptive Bitrate

Users	Video Requests per Second	Transcoding Jobs	Storage Needs	Network Bandwidth	Key Changes
100	10	5	100 GB	50 Mbps	Single transcoding server, local storage
10,000	1,000	500	10 TB	1 Gbps	Multiple transcoding servers, CDN introduction
1,000,000	100,000	50,000	1 PB	100 Gbps	Distributed transcoding cluster, sharded storage, global CDN
100,000,000	10,000,000	5,000,000	100 PB+	10 Tbps+	Massive distributed transcoding, multi-region CDN, advanced caching

First Bottleneck

The first bottleneck is the transcoding servers' CPU and GPU capacity. Transcoding video streams into multiple bitrates is CPU/GPU intensive. As user requests grow, transcoding jobs queue up, causing delays.

Scaling Solutions

• Horizontal Scaling: Add more transcoding servers or GPU instances to handle more concurrent jobs.
• Caching: Cache popular bitrate versions to avoid repeated transcoding.
• Pre-transcoding: Transcode videos into multiple bitrates ahead of time instead of on-demand.
• CDN Usage: Use Content Delivery Networks to serve adaptive bitrate streams closer to users, reducing bandwidth and latency.
• Storage Sharding: Distribute video storage across multiple nodes to handle large data volumes efficiently.
• Load Balancing: Distribute transcoding jobs evenly to prevent server overload.

Back-of-Envelope Cost Analysis

• At 10,000 users: ~1,000 video requests/sec, ~500 concurrent transcoding jobs.
• Storage: ~10 TB for multiple bitrate versions.
• Network: ~1 Gbps bandwidth needed for streaming.
• Transcoding servers: Need ~10-20 servers with GPUs to handle load.
• At 1M users: ~100,000 requests/sec, requiring ~50,000 transcoding jobs capacity.
• Storage grows to ~1 PB, network bandwidth ~100 Gbps.

Interview Tip

Start by explaining the transcoding process and adaptive bitrate basics. Identify the CPU/GPU bottleneck early. Discuss pre-transcoding vs on-demand transcoding trade-offs. Mention caching and CDN to reduce load. Structure your answer by scale and corresponding solutions.

Self Check

Your transcoding servers handle 1000 concurrent jobs. Traffic grows 10x. What do you do first?

Answer: Add more transcoding servers horizontally and implement caching or pre-transcoding to reduce on-demand load.

Key Result

Transcoding servers' CPU/GPU capacity is the first bottleneck as user requests grow; horizontal scaling combined with caching and CDN usage effectively addresses scalability.