Streaming at Scale: Architecting for 100M+ Concurrent Viewers

Hook — the nightmare you’re trying to avoid

Nothing wakes up platform engineers at 02:00 like a live event that suddenly draws tens of millions of viewers. You need an architecture that survives unpredictable peaks, keeps latency low, and preserves an excellent playback experience across millions of devices. Using JioHotstar’s record engagement during the 2025–2026 cricket season as a case study, this manual translates real-world lessons into a reproducible, technical playbook for handling 100M+ concurrent viewers — from ingest to edge delivery and autoscaling.

In brief: the single-slide summary

Design principle: decentralize and pre-warm. Spread risk across ingest, encoding, origin, and CDN; automate predictive scaling; and optimize ABR & chunking for low-latency delivery.

Key building blocks: resilient ingest (SRT/QUIC), distributed transcoding (AV1/HEVC fallback), chunked-CMAF packaging, multi-/private-CDN tiering, server-side ad insertion (SSAI) at edge, and predictive autoscaling tied to real-time telemetry.

Why JioHotstar matters in 2026

In January 2026, industry reporting highlighted JioHotstar’s record metrics: roughly 99 million concurrent digital viewers for a major cricket final and an average of 450 million monthly users across the platform. These figures are not just PR — they reflect real engineering problems solved at massive scale: origin resilience, CDN capacity planning, adaptive bitrate optimization for varied network conditions, and ad-insertion consistency across devices (Variety, Jan 2026).

"JioHotstar reported 99 million digital viewers for the cricket final and 450M monthly users, setting new expectations for streaming scale." — industry reporting, Jan 2026

Architecture blueprint: high level

Below is an enterprise-grade architecture that maps to the problems JioHotstar solved and to modern 2026 streaming patterns.

• Multi-regional ingest layer (edge PoPs for capture, redundant SRT/QUIC feeds)
• Hybrid encoding & packaging layer (on-prem + cloud burst, hardware accelerated AV1/HEVC)
• Origin & cache tier (tiered origins with pre-warm, object-store + hot origin)
• Multi-/private-CDN delivery with edge compute for SSAI & manifest manipulation
• Autoscaling & orchestration driven by predictive ML and event metadata
• Observability & test harness (synthetics, real-user telemetry, and chaos testing)

1) Ingestion: collect first, lose nothing

Protocols and redundancy

Use multiple transport protocols from venue encoders to your ingest PoPs. In 2026, the standard mix is:

• SRT for secure, reliable transport with packet recovery
• QUIC/HTTP/3 or WebTransport for low-latency ingest where supported
• RTMP/RTSP legacy bridges (only where unavoidable)

Deploy dual simultaneous feeds from each venue (primary and backup) to separate regional PoPs. Design your ingest to accept N parallel feeds and reconcile timing with ingest sequence numbers.

Edge collectors and pre-processing

Place lightweight collectors in metro PoPs to perform:

• Initial packet de-duplication and jitter buffering
• Timestamp alignment using PTS/NTSC/PTS correction
• Realtime metadata injection (score, game-state) for downstream SSAI

Lightweight edge collectors are similar in spirit to portable AV kits and pop-up playbooks used for compact live events — they prioritize minimal processing at the edge and rapid handoff to centralized packagers.

Sample ffmpeg ingest command (for an SRT feed)

ffmpeg -i srt://ingest.example.com:9000?mode=listener -c:v copy -c:a copy -f flv -metadata title="Match A" rtmp://processing.local/live/stream

2) Encoding & Packaging: ABR ladders and codecs in 2026

By 2026, AV1 is common for high-efficiency delivery, but hardware decoder availability still varies by device. Use an adaptive codec strategy:

• Primary: AV1 for 1080p and above where devices support it
• Fallback: HEVC (H.265) for devices with hardware support
• Compatibility: AVC (H.264) for older devices and low-power endpoints

Use hardware-accelerated encoders (AWS Nitro, NVIDIA NVENC/AV1 chips, Intel QuickSync) at scale and cloud-burst for peak events.

Chunked-CMAF and low-latency delivery

Implement Chunked-CMAF to support LL-HLS and LL-DASH. Chunked-CMAF with HTTP/3 reduces end-to-end latency and improves throughput on lossy networks. By 2026, major platforms use chunked-CMAF as the backbone for under-3s low-latency streaming.

Packaging example — Shaka Packager CLI

packager \
  in=video.av1.mp4,stream=video,init_segment=video_init.mp4,segment_template=video_$Number$.m4s \
  in=audio.mp4,stream=audio,init_segment=audio_init.mp4,segment_template=audio_$Number$.m4s \
  --profile live --hls_master_playlist_output=master.m3u8 --mpd_output=manifest.mpd

3) Origin and CDN strategy: multi-tier, multi-CDN, and private CDN

For extreme events, use a tiered delivery architecture:

1. Hot origin cluster: geographically distributed origins with SSD-backed caching for recent segments
2. Cold object store: S3/compatible store for longer-term segments and backups
3. Private CDN layer: a peering-focused delivery tier you operate for core regions (optional)
4. Public CDN partners: multi-CDN with traffic steering and geo-aware load balancing

JioHotstar-level events require active traffic steering and a multi-CDN contract to ensure capacity and SLAs. Negotiate bring-your-own-origin (BYO) peering with CDNs to reduce hop-count and latency. Many of the operational playbooks for small, local delivery tiers mirror recommendations in the pop-up tech field guide, where minimizing hops and pre-warming nodes is central to reliability.

Cache-key & TTL strategies

Use short TTLs for live segments (200–800 ms targets per chunk) but a longer TTL for master manifests to reduce manifest churn. Normalize cache keys to ignore ephemeral query params and append a shard header for A/B testing and per-device profiles.

4) Autoscaling & orchestration: beyond reactive HPA

Reactive autoscaling (CPU/memory-based) is necessary but insufficient. Use predictive scaling triggered by:

• Event schedules and ticket sales data
• Real-time pre-event growth signals (webhooks from promoter sites)
• ML models trained on historical peaks and network telemetry

Implement hybrid scaling:

• Scheduled reserves: minimum capacity reserved in the cloud and in private PoPs
• Predictive scale-up: provision encoders and packagers minutes before expected peaks
• Rapid reactive scale: Kubernetes HPA with custom metrics (segments/sec, connections/sec)

Kubernetes HPA example (custom metric)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: transcoder-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: transcoder
  minReplicas: 10
  maxReplicas: 500
  metrics:
  - type: Pods
    pods:
      metric:
        name: segments_per_second
      target:
        type: AverageValue
        averageValue: 50

5) Buffering, latency and ABR tradeoffs

Design clear SLOs for latency and buffering. For live sports, the goal is often sub-5s glass-to-glass latency for premium viewers and sub-15s for mass delivery. Tradeoffs:

• Lower latency = smaller session buffers = increased rebuffer risk on poor networks
• Higher ABR ladder granularity reduces bitrate switches but increases manifest size

Practical recommendations:

• Use a two-tier ABR ladder: high-fidelity narrow steps above 720p; coarse steps below to avoid oscillation
• Prefer player-side buffer targetting with delay-based ABR (BBA) to avoid unnecessary quality shifts
• Implement client-side rebuffer mitigation (e.g., forward error correction for critical segments)

6) Ads, personalization, and SSAI at scale

For ad-driven platforms, integrating SSAI at the edge avoids client-side fragmentation. Use edge functions (Workers or Lambda@Edge equivalents) to perform token validation, manifest stitching, and ad decisioning with a fallback cache of pre-fetched ads.

Pre-warm ad caches in every CDN region and provide low-latency decision endpoints globally. Adopt standardized ad signals (VAST + SCTE-35 + JSON metadata) to simplify ad stitching across vendors.

7) Observability, SLOs and synthetic testing

Create three classes of telemetry:

• Service telemetry: ingest success rate, encoder CPU/GPU metrics, packaging latency
• Delivery telemetry: CDN hit ratio, backbone latency, e2e p95 latency
• Client QoE telemetry: startup time, rebuffer rate, bitrate switches per session

Set SLOs and error budgets per region. Run synthetic testing every 30s from regional vantage points and maintain an active “canary” stream that mirrors production but returns a golden stream for regression detection.

8) Testing, rehearsals, and operational playbooks

Never trust capacity figures alone. Rehearse with scaled load tests simulating both normal and adversarial traffic patterns. Include:

• Full-stack rehearsals (ingest → packaging → CDN) with 10–20% of expected peak
• Stress tests for CDN cache-miss storms by forcing origin fetches
• Chaos experiments to validate failover between CDNs and origins

Maintain an incident playbook that includes runbooks for:

• Origin overload
• CDN outages (failover to alternate CDN / private CDN)
• Encoding backpressure (drop non-essential renditions)

9) Security, DRM and compliance

Secure all content paths. Use tokenized manifests, per-session keys, and modern DRM (Widevine, PlayReady, FairPlay) delivered via cloud KMS with a short-lived key rotation policy. For large sports rights, integrate forensic watermarking and watch for abnormal downstream distribution patterns.

10) Device & product manuals: deliverables for ops and device teams

To support cross-functional teams, produce a set of modular manuals and viewers optimized for field use and offline access:

• Operator Playbook PDF — incident playbooks, command recipes, runbooks, and checklists (printable)
• DevOps Manual — CI/CD pipelines, Kubernetes manifests, HPA examples, and monitoring configs
• Device Integration Guide — decoder capability matrix, codec fallbacks, manifest schemas
• Edge Function Templates — SSAI, manifest manipulation, token validation examples

Each manual should include:

1. Quick-start checklist (1–page)
2. Pre-event checklist (24h, 6h, 1h)
3. Runbook snippets (copy/paste CLI and YAML)
4. Recovery maps and contact escalation matrices

Example Pre-event checklist (1 hour before kickoff)

• Confirm primary and backup ingest feeds are live and in sync
• Confirm encoder pool utilization & GPU headroom ≥ 25%
• Verify CDN pre-warm API called in top 10 regions
• Run synthetic playback from 6 regions and verify startup time ≤ 3s
• Confirm ad caches prefilled and SSAI endpoints responsive

11) Example operational scripts and configs

Include it in your manuals: scripts for pre-warm API calls, origin health checks, and CDN failover. Example: CDN pre-warm via curl:

for url in $(cat segments_list.txt); do
  curl -s -o /dev/null -H "Cache-Control: no-cache" https://cdn.example.com/$url
done

Collect scripts and templates from field playbooks and event design guides so production teams can reuse tested commands during rehearsals.

12) Cost and capacity planning

Plan for three cost buckets:

• Fixed reserved capacity: permanent PoPs and license fees
• Elastic burst: cloud transcoding & egress (plan 2–5x normal usage)
• Operational overhead: monitoring, logging, ad decisioning, watermarking

Use game-theory planning: buy reserved capacity for predictable base load, contract cloud burst for peaks, and negotiate SLAs with CDN partners for capacity on demand. For small teams running regional events, the pop-up tech field guide contains practical budgeting templates that scale to marquee events.

2026 trends and future predictions

Looking forward from 2026, expect:

• Broader hardware AV1/VVC decoding support in consumer devices, lowering egress bitrate costs
• Expansion of HTTP/3 + WebTransport for low-latency interactivity (stats, chat, TTI)
• Edge-native SSAI and packaging to reduce origin dependence
• Stronger adoption of predictive ML for autoscaling, reducing cold-start failures
• Standardized security APIs for watermarking and forensic tracing

Practical takeaways — a 10-point checklist

1. Run dual redundant ingest paths (SRT + QUIC) from each venue.
2. Deploy AV1 as primary with HEVC/H.264 fallbacks.
3. Use chunked-CMAF for sub-5s low-latency streaming.
4. Tier your origin and pre-warm CDNs ahead of peaks.
5. Implement multi-CDN with automated traffic steering.
6. Combine scheduled reservations and predictive autoscaling.
7. Pre-warm ad caches and use edge SSAI for consistent monetization.
8. Instrument e2e telemetry and set regional SLOs.
9. Run rehearsals with at least 20% of expected peak RPS.
10. Publish operator and device manuals as downloadable PDFs with checklists and runbooks.

Case study: how JioHotstar operationalized scale (extracted lessons)

Key lessons from the JioHotstar example:

• Expect massive regional concentration of traffic — design for regional saturation rather than uniform global distribution.
• Negotiate capacity and peering (private CDN arrangements) months in advance for marquee events.
• Use real-time orchestration with pre-warmed cloud encoder capacity and prioritized evictions for non-critical streams.
• Invest in product UX for latency-sensitive viewers (second-screen sync and minimal ad latency).

Conclusion & call to action

Handling 100M+ concurrent viewers is a solvable engineering problem when you combine the right architecture, tools, and operational rigor. Learn from the hard-earned lessons of platforms like JioHotstar: decentralize ingest, embrace modern codecs and chunked-CMAF, pre-warm and tier your CDN/Origins, and move autoscaling from reactive to predictive.

Actionable next steps: download the printable Operator Playbook PDF (includes checklists, YAML manifests, and CLI scripts), run a 20% capacity rehearsal within 14 days, and instrument a predictive autoscaler against past event data.

Want the ready-to-use template set — operator playbook, Kubernetes manifests, and pre-warm scripts — packaged as a single downloadable PDF? Contact our team or request the manual to get a tailored review of your architecture before your next major event.

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