Why live content requires powerful servers and CDNs

1) What is the "severity" of live compared to VOD

Real-time fan out. One incoming stream → thousands of outgoing streams. Any drawdown of the CPU/network instantly hits all viewers.

Hard SLAs by delay. In live, not only "picture" is important, but also "today's air": 0.5-2 s for WebRTC and 2-5 s for LL-HLS.

Permanent encoding/transcoding. You need to keep several bitrate stairs (ABR) and profiles for different screens/networks.

Unstable viewer network. Requires adaptive bitrates, resets, GOP rebuild, and aggressive buffers at peaks.

Inability to "fix it later." VOD can be re-branded. In live, frame error is a lost moment forever.

2) Servers for encoding and transcoding: CPU, GPU, presets

Codecs: H.264/AVC - gold standard compatibility; HEVC/AV1 - save traffic, but harder to encode and decode on weak devices.

Iron:

CPU x264 (veryfast-faster) - stability, predictability, but expensive in cores.
GPU NVENC/AMF/Quick Sync - cheap to stream, useful for ABR stairs.
Low delay settings: short GOP (1-2 sec), limited B-frames, CBR/conservative VBR, regular keyframes for quick profile switches.
Why "powerful": a couple of dozen simultaneous 1080p60 profiles already abut the server against CPU/GPU and memory, especially with multi-leaf ABR.

3) WebRTC, SFU and TURN: where "real" power is needed

SFU (Selective Forwarding Unit). Does not mix, but routes streams → saves CPU, but requires a wide egress and competent fan-out.

TURN/ICE/STUN. With NAT/firewalls, traffic goes through TURN - this is a full relay, doubling the load on uplink.

Backpressure and prioritization. When overloaded, the SFU must lower the quality/frame rate, otherwise it will break the session.

Why CDN is not enough. WebRTC is poorly cached by traditional CDN - the load falls on the media server layer (SFU clusters).

4) LL-HLS/DASH and CDN: How to scale viewers

Segment cacheability. Unlike WebRTC, HLS/DASH segments are cached on edge → the load on origin is dramatically reduced.

Origin-shield and multilevel CDN. Edge → regional → origin cache nodes. High cache hit ratio is critical to save egress/CPU.

ABR ladders. 240p-1080p (sometimes 1440p/2160p). The more profiles, the higher the load on the transcoder and storage.

Multi-CDN. Anycast/DNS-steering, real-user measurements (RUM) and automatic fake by load/error time metrics.

5) Consistency of time and events

For interactive live scenarios (bets, quizzes, live casinos):

Hard time synchronization (NTP/chrony), 'video _ ts' marks in events and server "source of truth."
Message sequence (seq, ACK, retransmit, idempotency).
Replays and recording (WORM storage) for debriefing.

6) Example of capacity calculation (conservatively)

1080p stream with 4 Mbps ≈ bit rate.

Online at the same time: 20,000 viewers.

Total egress: 4 × 20,000 = 80,000 Mbps = 80 Gbps.

With 80% cache-hit on edge, traffic from origin ≈ 20%: 16 Gbps.

For WebRTC (non-cached), if one SFU node stably holds an 8 Gb/s egress ~, you need to ≈ 10 SFU nodes + 2-3 in reserve.

💡 Conclusion: even "moderate" live quickly rests on network egress and horizontal scaling of media servers.

7) Record storage and timeshift

5 Mbps → 0.625 Mbps → ≈ 2.2 GB per hour per profile.

For 6 ABR profiles and 10 tables/channels: 2.2 × 6 × 10 = ≈ 132 GB/h.

Need "cold" storage layers + life cycles (tiering/TTL).

8) Typical bottlenecks

Transcoder CPU/GPU. Connection peaks → the growth of reshapes and GOP rebuilding.

SFU and TURN network. SNI locks, NAT symmetry → full relay and sudden load spire.

Disk subsystem origin. High QPS in small segments, especially in LL-HLS.

Memory and sockets. Thousands of WebSocket/DTLS sessions per kernel require kernel/epoll tuning and FD limits.

GC/RT pauses. On JVM/Node Media Gateways, configure GC and isolate hot paths.

9) Content security and protection

TLS termination on edge, HSTS, a modern set of ciphers.

Signed URL/tokens, short TTL, geo/ref restrictions.

DRM/LL-token for protected tapes.

Anti-scraping/anti-restream. Watermarks, behavioral cues, non-public manifestos.

10) Observability and SLO

Video metrics: e2e delay, freeze rate, frame misses, ABR profile downgrade percentage, decoder failures.

Network: throughput by points of presence, WebRTC reconnection, ICE/TURN, RTT/jitter errors.

Server: CPU/GPU load, temperature, ulimit, number of open sockets, p95/p99 by API.

Product: connection rate, hold, average session duration, complaint-rate.

SLO examples: 99.5% of segments are delivered <1.5 s; 95th WebRTC delay percentile ≤ 2.5 s; drop-frame < 1%.

11) Cost optimization without loss of quality

Coding hybrid: basic profiles on GPU, "beautiful" profiles for premium - on x264 CPU.

Content-aware encoding. Dynamic bitrates by scene (static/dynamic episodes).

Multi-CDN with price routing. Switching by aggregate quality/cost metric.

Reduce the number of profiles. If the audience is mobile, 720p often "holds its punch."

Edge-origin-shield. We increase cache-hit, reduce outgoing traffic from origin.

12) Checklist for launching live "at capacity"

Infrastructure

Transcoder cluster (CPU + GPU) with autoscale and hot standby.
SFU cluster for WebRTC + TURN pool with white IP and relay share monitoring.
Origin-shield and at least 2 independent CDNs.
Storage with TTL/Archive (WORM) policies for writes/replays.

Low latency

GOP ≤ 2 c, scheduled keyframes, CBR/low-latency presets.
ABR ladder optimized for mobile segment.
Real-time time time synchronization, 'video _ ts' marks in events.

Reliability

Multi-zone, flow feiler, automatic quality degrade instead of drop.
Tests for 1.5 × of planned load and storm of reconnections.
Full observability: metrics, logs, traces, alerts.

Safety

Signed URLs, short TTL, geo-constraints, DRM if necessary.
TLS on edge, certificate rotation, hotlink/restriction protection.
PII minimization, network segregation, access auditing.

13) Recipe for architecture by content role

Interactive (betting/quiz/live casino): WebRTC + SFU, ultra-low latency, parallel to LL-HLS as "visual" feed.

Mass audience broadcasts: LL-HLS/DASH + aggressive CDN, ABR optimization, recording and timeshift.

Hybrid: primary in WebRTC, mirroring in LL-HLS for replays and deferred browsing.

Live content is not just "video on the Internet." It is a real-time managed thread factory where media servers, encoders, SFUs, CDNs, and storage operate synchronously and under peak loads. Powerful servers are needed to keep encoding and fan-out without losing frames; CDN - to deliver millions of segments quickly and cheaply. In conjunction, they provide what viewers and interactive scenarios expect: a stable picture, low latency and scale, and business - predictable cost and SLA.