JioCinema successfully managed 32 million concurrent viewers during the IPL by implementing scalable infrastructure, CDN optimization, and personalized content management. The platform employed techniques like panic mode, graceful degradation, and Kafka for asynchronous processing to ensure zero downtime and a seamless user experience. Key strategies included preemptive database scaling, efficient caching policies, and advanced ad insertion mechanisms to handle high traffic while maintaining performance.

1. CURIOUS : HOW JIOCINEMA
HANDLED 32M CONCURRENT
USERS
A technical deep dive into streaming video content at scale
Present By : Ansal KA

2. JIOCINEMA’S
MULTI-CAM IPL
STREAMING
 JioCinema is an Indian OTT media
streaming service offering video
streaming services. Since 2023,
JioCinema has held the exclusive
digital rights to broadcast the
Indian Premier League (IPL) in
India.
 With multiple cameras available on
the ground, it is now possible for
viewers to switch to their
preference camera angle while
watching IPL.
 This includes spider camera view,
batsman view, bird's eye view,
wicketkeeper view and recently
added Hero Cam.
 Production Control Rooms (PCRs).

3. JIO CINEMA
STATISTICS
The platform reached 26 billion
views during the IPL 2024
season, marking a 53 per cent
increase from the previous year
During the IPL 2023 final, when
Dhoni came out to bat, the
digital streaming platform
JioCinema hit 22 million
concurrent viewers

4. INTRODUCTION
Challenge:
Managing the demands of
over 32 million concurrent
viewers during key IPL
moments (e.g., finals).
Goal:
Zero downtime, uninterrupted
streams, and seamless user
experiences.
Solution Focus:
Comprehensive audits,
scalable infrastructure, CDN
optimization, and
personalized content
management.
Key Points Covered:
CDN management
Backend scaling
Cache strategies
Panic Mode readiness

5. BASIC FLOW

6. FRONTEND OPTIMIZATION
 Feature Flags:
• Implemented for controlled feature rollouts to identify issues
early.
• Features can be rolled back instantly if causing app crashes
or performance drops.
• Enhances agility in handling real-time issues without
impacting the entire user base.

7. FRONTEND OPTIMIZATION
 Scenario Simulations:
• Tools like Charles used to simulate different network
conditions (low bandwidth, high latency).
• Test real-world scenarios like DNS failures, API call timeouts,
and backend errors to ensure preparedness.

8. FRONTEND OPTIMIZATION
 Graceful Degradation:
• When certain features fail, default content is shown (e.g.,
home screen UI fallback).
• Core , P1 and P2 Features classification
• Errors are hidden from the user to ensure continuous
satisfaction.
• Exponential Backoff technique for retrying failed API calls to
avoid server overload.

9. EXPONENTIAL
BACKOFF
A technique for handling failed HTTP
requests by gradually increasing the wait
time between retries to prevent server
overload.
For example, the first retry waits for 1
second, the second retry waits for 2
seconds, and so on. This helps prevent
server overloading.

10. BACKEND
INFRASTRUCTURE
 A content delivery network (CDN) is a
network of interconnected servers that
speeds up webpage loading for data-
heavy applications.
 By distributing content across multiple
servers located in different geographic
locations, CDNs can deliver content
faster and more efficiently to users.
 That way, it reduces latency, improves
page load times, and provides a better
user experience.

11. BACKEND
INFRASTRUCTURE
• In-house CDN optimizer to dynamically balance
traffic across multiple CDNs.
• Monitors real-time load on each CDN and routes
traffic accordingly to avoid overburdening any
single CDN.
• Reduces the risk of regional failures and improves
global load distribution.
Multi-
CDN
Strategy:
• Preemptive scaling of databases before big
matches.
• Simulations of expected load, including API calls,
database queries, and CPU utilization.
• Communicated scaling needs with cloud providers
months in advance to ensure readiness.
• Resource thresholds set at ~60% CPU utilization to
maintain high performance during peak times.
Databas
e
Scaling:

12. MULTI-CDN OPTIMIZATION
Sticker overload issue: Resolved by caching stickers and
Base64 encoding new stickers, reducing the burden on the
origin server.
Image
CDN:
Optimized for seamless video delivery during peak traffic.
Special strategies for handling video spikes during critical
match moments.
Video
CDN:
Cached frequent API calls (e.g., login, match stats) for
quicker responses.
Dynamic routing across multiple CDNs to ensure minimal
latency and improve user experience.
API CDN:

13. CACHE MANAGEMENT & OFFLOADING
Cache TTL Extension:
Longer Time-to-Live (TTL) on cached
content to reduce backend server load.
Popular content (e.g., high-traffic videos,
repeated API calls) is cached longer to
offload requests from the origin server.
Cache Offloading:
Optimized CDN caching policy with a
goal of >90% cache offloading.
Efficient eviction and refresh strategies to
ensure content is timely while minimizing
server strain.
Use of Base64 for efficient encoding of
new image-based assets (e.g., stickers).

14. PANIC MODE & SNAPSHOT-BASED FAILOVER
Panic Mode Activation:
Triggered when unexpected traffic surges
occur (e.g., Dhoni entering the field).
Pre-match snapshots of expected API
responses are stored in static storage.
If the system starts failing due to load, CDN
serves static content from these snapshots,
ensuring zero complaints from users.
Static Failover:
Customization systems like personalized
home screens may fail under load;
snapshots ensure standard content is
displayed.
Ensures continuous operation even in the
face of backend failures, reducing
pressure on real-time processing.

15. ASYNCHRONOUS PROCESSING WITH KAFKA
Kafka Utilization:
Heavy reliance on Kafka for handling
asynchronous tasks during live events.
Tasks such as viewer count updates are
processed asynchronously to reduce real-
time load on the backend.
Throughput Optimization:
Kafka ensures high throughput with
partitioning for better scalability.
Resilience: If Kafka is temporarily down,
data can be processed from local
storage to ensure service continuity.

16. LADDER-BASED SCALING DOWN
Smooth Downscaling:
Traffic doesn’t drop off immediately after
a match; users often linger.
Gradual downscaling strategy: from 32M
to 25M to 20M, ensuring smooth
transitions and no service interruptions.
Health Checks:
Liveness checks and readiness probes to
ensure that each component is
functioning as expected before scaling
down.
Optimizes resource use without abruptly
reducing capacity, avoiding
unnecessary service degradation.

17. AD INSERTION MECHANISM
• Ad directors monitor live matches and trigger ads based on
commentary cues.
• Different languages have dedicated ad directors for each
live stream.
Live Stream Ad
Insertion:
• Avoid over-delivery of ads by targeting segments of the 32M
users.
• Client-side ads triggered using SCTE-35 markers for precise
ad placement without disrupting user experience.
Static Ads &
Client-Side
Ads:

18. CONCLUSION
& FUTURE
READINESS
• Key Takeaways:
• Scalability: Proactive planning and simulations ensured
readiness for up to 32M users.
• Resilience: Panic Mode and static content delivery helped
prevent system crashes during high load.
• Efficiency: Efficient CDN and cache management played a
crucial role in handling traffic.
• Ad Scalability: Smooth ad insertion techniques ensured
monetization without affecting the user experience.
• Future Improvements:
• Further refinement of caching policies.
• Enhanced database simulations for faster scaling.
• Even greater automation in scaling and Panic Mode
activation.

19. QUESTIONS?
 References:
Medium: Behind the Scenes: How JioCinema
Seamlessly Streams IPL Matches to 20 Million Fans
Youtube : How JioCinema
live streams IPL to 20 million concurrent devices w/ Prachi S
harma | Ep 7