15-744 Computer Networking
Lecture 18 – Internet Video Delivery
Matt Mukerjee
Slides: Hui Zhang, Peter Steenkiste, Athula Balachandran,
Srini Seshan, et. al.
The Internet Today…
Video
Video
Could be Video
Probably Video
Encrypted Video?
Purchasing Videos?
Video in the name!
DEFINITELY Video!
Sharing Video!
Ads! (and Videos!)
2014 1H – NA Fixed Access
2
The Internet Today…
Video
Video
Could be Video
Probably Video
Encrypted Video?
Purchasing Videos?
Video in the name!
DEFINITELY Video!
Sharing Video!
Ads! (and Videos!)
2014 1H – NA Fixed Access
3
Demystifying Video Delivery
Video
Source
Screen
Video Player
???
Internet
What’s this?
Today’s lecture
4
Outline
•
•
•
•
•
Background on Video
History of Internet Video Delivery
CDNs & the World Today
Content Brokers & Quality of Experience
Live Video Delivery
5
Terminology
• Frame
• Resolution
• Number of pixels per frame
• 160x120 to 1920x1080 (1080p) to 4096x2160 (4K)
• FPS (frames per second)
• 24, 25, 30, or 60
• Bitrate
• Information stored/transmitted per unit time
• Usually measured in kbps to mbps
• Ranges from 200Kbps to 30 Mbps
6
Internet Video Requirements
• Smooth/continuous playback
• Elasticity to startup delay: need to think in terms of RTTs
• Elasticity to throughput
• Multiple encodings: 200Kbps, 1Mbps, 2 Mbps, 6 Mbps, 30Mbps
• Multiple classes of applications with different requirements
Delay
2, N-way
conference
Bandwidth
< 200 ms 4 kbps audio only,
200 kbps – 5 Mbps video
Examples
Skype, Google hangout,
Polycom, Cisco
Short form VoD < 1-5s
300 kbps – 2 Mbps & higher Youtube
Long form VoD
< 5-30s
500 kbps – 6 Mbps & higher Netflix, Hulu, Qiyi,
HBOGO
Live Broadcast
< 5-10s
500 kbps – 6 Mbps & higher WatchESPN, MLB
Linear Channel
< 60s
500 kbps – 6 Mbps & higher DirectTV Live
7
Avoid Buffering like the Plague!
Buffering… 51%
8
1990 – 2004: 1st Generation Commercial
PC/Packet Video Technologies
•
•
•
•
Simple video playback, no support for rich app
Not well integrated with Web browser
No critical mass of compelling content over Internet
No enough broadband penetration
9
2005: Beginning of Internet Video Era
100M streams first year
Premium Sports Webcast on Line
10
2006 – 2015: Internet Video Going Prime Time
2006
2007
2008
2009
2010
2011
11
First Generation: HTTP Download
• Browser request the object(s) and after receiving
them pass them to the player for display
• No pipelining
12
First Gen: HTTP Progressive Download (2)
• Alternative: set up connection between server and
player; player takes over
• Web browser requests and receives a Meta File
(a file describing the object) instead of receiving
the file itself;
• Browser launches the appropriate player and
passes it the Meta File;
• Player sets up a connection with Web Server and
downloads or streams the file
13
Meta file requests
14
Buffering Continuous Media
• Jitter = variation from ideal timing
• Media delivery must have very low jitter
• Video frames every 30ms or so
• Audio: ultimately samples need <1ns jitter
• But network packets have much more jitter
that that!
• Solution: buffers
• Fill buffer over the network with best effort service
• Drain buffer via low-latency, local access
15
Max Buffer Duration
"Bad": Buffer
overflows
= allowable jitter
Max Buffer Size
File Position
Streaming, Buffers and Timing
Buffer
Duration
"Good" Region:
smooth playback
Buffer
Size
"Bad": Buffer
underflows and
playback stops
Buffer almost empty
Time
16
Drawbacks of HTTP Progressive Download
• HTTP connection keeps data flowing as fast as possible
to user's local buffer
• May download lots of extra data if user does not watch the entire
video
• TCP file transfer can use more bandwidth than necessary
• Mismatch between whole file transfer and stop/start/seek
playback controls.
• However: player can use file range requests to seek to video
position
• Cannot change video quality (bit rate) to adapt to network
congestion
17
2nd Generation: Real-Time Streaming
• Build our own protocol! This gets us around HTTP;
app layer protocol can be better tailored to Streaming
Multimedia
18
Example: Real Time Streaming Protocol (RTSP)
19
Drawbacks of Real-Time Streaming
• Per-client state!
• Streaming protocols often blocked by routers
• HTTP delivery can use ordinary proxies and
caches
• Conclusion: rather than adapt Internet to
streaming, adapt media delivery to the
Internet
20
3rd Generation: HTTP Streaming
• Client-centric architecture with stateful client and stateless
server
• Standard server: Web servers
• Standard Protocol: HTTP
• Session state and logic maintained at client
•
•
•
•
Video is broken into multiple chunks (like with bittorrent)
Each chunk can be played independent of other chunks
Playing chunks in sequence gives seamless video
Meta-file (manifest) provides chunk file names
21
HTTP Chunking Protocol
Manifest:
A1, A2, …
Server
Client
Manifest
HTTP
Adaptive
HTTP GET Manifest
Player
A1
A1
A2 …
Manifest
A1
A2 …
Cache
Web browser
Web server
HTTP
HTTP
TCP
TCP
Web server
22
HTTP Chunking Protocol
Manifest:
A1, A2, …
Server
Client
Manifest
HTTP
Adaptive
HTTP
HTTPGET
GETA2A1
Player
A1
A
A11
A
A22 …
A1
A2 …
Cache
Web browser
Web server
HTTP
HTTP
TCP
TCP
Web server
23
Adaptive Bitrate with HTTP Streaming
• Encode video at different quality/bitrate
levels
• Client can adapt by requesting chunks at
different bitrate levels
• Chunks of different bitrates must be
synchronized
• All encodings have the same chunk boundaries
and all chunks can be played independently, so
you can make smooth splices to chunks of higher
or lower bit rates
24
HTTP Chunking Protocol
Client
Manifest:
LQ: A1, A2, …
HQ: B1, B2, ...
Server
Manifest
HTTP
Adaptive
HTTP
2 1
HTTPGET
GETBA
Player
A1
Cache
B1
A
A11
A2 …
B1
B
B22
A1
A2 …
B1
B2
…
Web browser
Web server
HTTP
HTTP
TCP
TCP
…
Web server
25
Advantages of HTTP Streaming
• Easy to deploy: it's just HTTP!
• Work with existing caches/proxies/Webservers/Firewall
• Fast startup by downloading lowest quality/smallest
chunk
• Bitrate switching is seamless
• Many small files
• Small with respect to the movie size
• Large with respect to TCP
• 5-10 seconds of 1Mbps – 3Mbps  0.5MB – 4MB per
chunk
26
Demystifying Video Delivery
Video
Source
Screen
Video Player
???
Internet
27
Demystifying Video Delivery
Video
Source
Screen
Video Player
Encoders &
Video
Servers
ISP & Home
Net
CMS
and
Hosting
???
Internet
28
Demystifying Video Delivery
Video
Source
Screen
Video Player
Encoders &
Video
Servers
CMS
and
Hosting
ISP & Home
Net
Content
Delivery
Networks
(CDN)
29
Outline
•
•
•
•
•
Background on Video
History of Internet Video Delivery
CDNs & the World Today
Content Brokers & Quality of Experience
Live Video Delivery
30
Content Delivery Networks (CDNs)
• Goal: deliver content from optimal locations
• (Generally: users in Pittsburgh go to Pittsburgh server,
users in Tokyo go to Tokyo server, …)
• Customers (e.g., CNN, ESPN, …) pay CDN
to deliver their content
• Variety of companies: Akamai, Limelight,
Level 3, …
• Akamai is one of the most well known
• 170,000+ servers in 102 countries
• 15-30% of internet traffic
31
How Akamai Works
cnn.com (content provider)
DNS root server
Akamai server
Get foo.jpg
11
Get
index.
html
1
10
2
3
4
5
Akamai high-level DNS server
6
7
Akamai low-level DNS server
8
Akamai server
9
End-user
12
Get /cnn.com/foo.jpg
32
Akamai – Subsequent Requests
cnn.com (content provider)
Get
index.
html
1
DNS root server
Akamai server
Akamai high-level DNS server
2
7
Akamai low-level DNS server
8
Akamai server
9
End-user
12
Get /cnn.com/foo.jpg
33
Outline
•
•
•
•
•
Background on Video
History of Internet Video Delivery
CDNs & the World Today
Content Brokers & Quality of Experience
Live Video Delivery
34
What’s wrong with this picture?
Video
Source
Screen
Video Player
Encoders &
Video
Servers
CMS
and
Hosting
ISP & Home
Net
Content
Delivery
Networks
(CDN)
35
Closing the Loop with Analytics
Content Broker
Video
Source
Screen
Video Player
Encoders &
Video
Servers
CMS
and
Hosting
ISP & Home
Net
Content
Delivery
Networks
(CDN)
36
Closing the Loop with Analytics
Video
Source
Screen
Video Player
Encoders &
Video
Servers
CMS
and
Hosting
ISP & Home
Net
Content
Delivery
Networks
(CDN)
37
Why Analytics?
• Analytics help judge users’
Quality of Experience (QoE)
• Users’ perception of quality greatly impacts
engagement
• (and thus ad revenue)
Conviva
38
How can Conviva Help increase QoE?
• Runs code directly in clients browser, from
customer website (e.g., CNN, ESPN, …)
• Gathers historic and real-time data from
each client
• Predict which CDN will provide the best
QoE for a given client
• (and send the client there)
• Find the optimal time to switch bitrates
39
Conviva Architecture
40
Simple, Right?
• Turns out no single metric is a good
indicator of QoE! 
41
Commonly used Quality Metrics
Join Time
Buffering ratio
Rate of switching
Rate of buffering
Average Bitrate
42
Commonly used Quality Metrics
• Need some function that maps metrics to
QoE
• Use machine learning? ✓
• Biggest problem… confounding factors!
• (e.g., live video and VoD behave very
differently)
43
Challenge: Confounding Factors
Live and VOD sessions experience similar quality
44
Challenge: Confounding Factors
However, user viewing behavior is very different
45
Confounding Factors
Device
Type of Video
Popularity
Location
Connectivity
Time of day
Day of week
Need systematic approach to
identify and incorporate confounding
factors
46
How to Rank Confounding Factors?
• Relative information gain
• ML for “how key is a particular factor”
• Decision tree structure
• Does this factor drastically change the DT
structure?
• Tolerance
• Do users tolerate changes in this factor more
easily?
47
Identifying Key Confounding Factors
Factor
Relative
Decision
Information Tree
Gain
Structure
Tolerance
Level
✓
✗
✓
✗
✓
✗
Time of day
✗
✗
✗
✗
✗
✓
✗
✗
✗
✓
✓
✓
Day of week
✗
✗
✗
Type of video
Popularity
Location
Device
Connectivity
48
Identifying Key Confounding Factors
Factor
Relative
Decision
Information Tree
Gain
Structure
Tolerance
Level
✓
✗
✓
✗
✓
✗
Time of day
✗
✗
✗
✗
✗
✓
✗
✗
✗
✓
✓
✓
Day of week
✗
✗
✗
Type of video
Popularity
Location
Device
Connectivity
49
Results
• Use important confounding factors to create
separate QoE estimator models (using ML)
• 20% increase in user engagement
50
Outline
•
•
•
•
•
Background on Video
History of Internet Video Delivery
CDNs & the World Today
Content Brokers & Quality of Experience
Live Video Delivery
51
Live Video
• Live video is becoming immensely popular
• Demand increases 4-5x every three years
• Expected to reach 50 Tbps this year
• Amazon buys Twitch for ~$1Billion
• Users watch 150b min of live video per month
• Single World Cup stream = 40% of Internet
traffic
52
Live Video Challenges
•
•
•
•
Amount of Demand
Can’t Cache
Workload Variety (e.g. mega-events / longtail)
Users want:
• Good QoE (good bitrate, buffering ratio, join time)
• CDNs want:
• meet user demand, minimize delivery cost
• WAN imposes:
• latency, packet drops, failures, …
53
CDN Live Video Delivery Background
Video
Sources
Data Response
Interior
Clusters
HTTP GET
Edge
Clusters
Video Requests
Distribution Trees
Channel 1
Channel 2
CDN Live Video Delivery Background
CDN(
A(
C(
B(
D(
Sources(
Reflectors(
Edges(
Video(Requests(
DNS(
AS/Clients(
Chan.(1(
Chan.(2(
High(Cost(
Low(Cost(
Questions?
56
15-744 Computer Networking
Lecture 18 – Internet Video Delivery
Matt Mukerjee
Slides: Hui Zhang, Peter Steenkiste, Athula Balachandran,
Srini Seshan, et. al.