Courses/Computer Science/CPSC 441.W2014/Chapter 7: Multimedia Networking
Course Overview |
Application Layer |
Transport Layer |
Network Layer |
Datalink Layer |
Advanced Topics |
Extra |
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Chapter 7 |
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Contents
Introduction
Hello, my name is Carrie Mah and I am currently in my 3rd year of Computer Science with a concentration in Human Computer Interaction. I am also an Executive Officer for the Computer Science Undergraduate Society. If you have any questions (whether it be CPSC-related, events around the city, or an eclectic of random things), please do not hesitate to contact me.
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Chapter 7: Multimedia Networking
- Notes adapted from slides created by JFK/KWR and lectures held by Dr. Carey Williamson
- All material copyright 1996-2012 © J.F Kurose and K.W. Ross, All Rights Reserved
Section 7.1: Multimedia Networking Applications
Multimedia: Audio
- Analog audio signal sampled at constant rate
- Telephone: 8,000 samples/sec
- CD music: 44,100 samples/sec
- Each sample quantized, i.e. rounded
- E.g. 28=256 possible quantized values
- Each quantized value represented by bits, e.g. 8 bits for 256 values
- Example: 8,000 samples/sec, 256 quantized values: 64,000 bps
- Receiver converts bits back to analog signal:
- Some quality reduction
- Example rates
- CD: 1.411 Mbps
- MP3: 96, 128, 160 kbps
- Internet telephony: 5.3 kbps and up
- Content types
- Voice, video, data, etc.
- Voice has low demand on data rate on data sharing
- Video has higher demand
- Audio formats
- Human voice over telephone
- Constant
Multimedia: Video
- Video: sequence of images displayed at constant rate
- E.g. 24 images/sec
- Digital image: array of pixels, spatial redundancy
- Coding: use redundancy within and between images to decrease number of bits used to encode image
- Spatial (within image)
- Instead of sending N values of same color (all purple), send only two values: color value (purple) and number of repeated values (N)
- Each pixel represented by bits
- Similar pixels in video still image can be encoded
- Temporal (from one image to next)
- Instead of sending complete frame at i+1, send only the differences from frame i
- CBR: (constant bit rate): video encoding rate fixed, variable quality to video
- VBR: (variable bit rate): constant quality to video
- Video encoding rate changes as amount of spatial, temporal coding changes
- Fluctuates based on the amount of changes (in motion, details, etc.) in the scene
- Examples:
- MPEG 1 (CD-ROM) 1.5 Mbps
- MPEG2 (DVD) 3-6 Mbps
- MPEG4 (often used in Internet, < 1 Mbps)
- Encoded in more data intensive format
- Higher frame rate: typically 24 fps or 30 fps
- Spatial redundancy
- I.e. similar
Multimedia Networking: Application Types
- Streaming/stored audio, video
- Streaming: can begin playout before downloading entire file
- Stored (at server): can transmit faster than audio/video will be rendered (implies storing/buffering at client)
- E.g. YouTube, Netflix, Hulu
- Conversational voice/video over IP
- Interactive nature of human-to-human conversation limits delay tolerance
- E.g. Skype
- Streaming live audio, video
- E.g. live sporting event (futbol)
Section 7.2: Streaming Stored Video
Challenges
- Continuous playout constraint: once client playout begins, playback must match original timing
- But network delays are variable (jitter), so will need client-side buffer to match playout requirements
- Other challenges:
- Client interactivity: pause, fast-forward, rewind, jump through video
- Video packets may be lost, retransmitted
- Client-side buffering and playout delay: compensate for network-added delay, delay jitter
- On server side video is stored (states how many fps)
- When streaming, video needs to be sent at same rate
Client-side Buffering, Playout
- (1) Initial fill of buffer until playout begins at tp
- (2) Playout begins at tp
- (3) Buffer fill level varies over time as fill rate x(t) varies and playout rate r is constant
- Playout buffering: average fill rate (x), playout rate (r):
- x < r: buffer eventually empties (causing freezing of video playout until buffer again fills)
- x > r: buffer will not empty, provided initial playout delay is large enough to absorb variability in x(t)
- Initial playout delay tradeoff: buffer starvation less likely with larger delay, but larger delay until user begins watching
- When there's a delay in the sending of frames for video over the network
- Needs to be a client side buffering to have proper playback
Streaming Multimedia
Download+Play | Streaming | Real-Time Transfer Protocol (RTP) | RTCP - RTP Control Protocol | Real-Time Streaming PRotocol (RTSP) |
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UDP vs TCP
- At transport layer
- Early days: all video streaming was UDP (video quality poor)
- Recent trend is TCP based streaming
- To get through firewalls
- Firewall configurations tend to disable UDP
- Other protocols: RTP, RTSP, HTTP, DASH
UDP
- Server sends at rate appropriate for client
- Often: send rate = encoding rate = constant rate
- Transmission rate can be oblivious to congestion levels
- Short playout delay (2-5 seconds) to remove network jitter
- Error recovery: application-level, time permitting
- RTP [RFC 2326]: multimedia payload types
- UDP may not go through firewalls
HTTP
- Multimedia file retrieved via HTTP GET
- Send at maximum possible rate under TCP
- Fill rate fluctuates due to TCP congestion control, retransmissions (in-order delivery)
- Larger playout delay: smooth TCP delivery rate
- HTTP/TCP passes more easily through firewalls
- End-to-end control of TCP between sender and receiver buffer
- Media player – application buffer for playout
DASH
- Dynamic, Adaptive Streaming over HTTP
- Server:
- Divides video file into multiple chunks
- Each chunk stored, encoded at different rates
- Manifest file: provides URLs for different chunks
- Client:
- Periodically measures server-to-client bandwidth
- Consulting manifest, requests one chunk at a time
- Chooses maximum coding rate sustainable given current bandwidth
- Can choose different coding rates at different points in time (depending on available bandwidth at time)
- Video encoded at multiple bit rates
- Dynamically choose between them based on available bandwidth
- Measure delivery rate over network periodically, dynamically choose the piece
- "Intelligence" at client: client determines:
- When to request chunk (so that buffer starvation, or overflow does not occur)
- What encoding rate to request (higher quality when more bandwidth available)
- Where to request chunk (can request from URL server that is “close” to client or has high available bandwidth)
Content Distribution Networks
- Challenge: how to stream content (selected from millions of videos) to hundreds of thousands of simultaneous users?
- Option 1: single, large "mega-server"
- Single point of failure
- Point of network congestion
- Long path to distant clients
- Multiple copies of video sent over outgoing link
- Quite simply: this solution doesn’t scale
- Option 2: store/serve multiple copies of videos at multiple geographically distributed sites (CDN)
- Enter deep: push CDN servers deep into many access networks
- Close to users
- Used by Akamai, 1700 locations
- Bring home: smaller number (10’s) of larger clusters in POPs near (but not within) access networks
- Used by Limelight
- Used commercially, popular in cloud-based delivery
- Move content closer to clients and get mirrored copies of it delivered to east/west coast/internal destinations, etc.
Example: 'Simple' Content Access Scenario
- Bob (client) requests video http://netcinema.com/6Y7B23V
- Video stored in CDN at http://KingCDN.com/NetC6y&B23V
- (3) Cinema redirects Bob
- (4)Bob finds out where video is
- (6) Movement of video done with KINGCDN server instead of netcinema.com
CDN Cluster Selection Strategy
- Challenge: how does CDN DNS select “good” CDN node to stream to client?
- Pick CDN node geographically closest to client
- Pick CDN node with shortest delay (or minimum number of hops) to client (CDN nodes periodically ping access ISPs, reporting results to CDN DNS)
- IP anycast
- Alternative: let client decide - give client a list of several CDN servers
- Client pings servers, picks 'best'
- Netflix approach
Case Study: Netflix
- 30% downstream US traffic in 2011
- Owns very little infrastructure, uses 3rd party services:
- Own registration, payment servers
- Amazon (3rd party) cloud services:
- Netflix uploads studio master to Amazon cloud
- Create multiple version of movie (different endodings) in cloud
- Upload versions from cloud to CDNs
- Cloud hosts Netflix web pages for user browsing
- Three 3rd party CDNs host/stream Netflix content: Akamai, Limelight, Level-3
- Infrastructure is minimal
- Accounting servers keeps money
- Everything else is farmed out
- Gets all different versions of Amazon cloud and those versions sent to CDNs and dash streaming