Reza Rejaie

1
PALS Peer-to-Peer Adaptive Layers Streaming

• Reza Rejaie
• Computer and Information Science Department
• University of Oregon
• Antonio Ortega
• Integrated Media Systems Center
• University of Southern California
• NOSSDAV 2003
• Monterey, California
• 6/3/2003

2
Motivation

• Peer-to-Peer (P2P) networks emerge as a new
  communication paradigm that improves
• Scalability, robustness, load balancing, etc
• Research on P2P network has mostly focused
  locating a desired content, not on delivery
• Audio/Video streaming applications are becoming
  increasingly popular over P2P network
• streaming a desired content to a peer rather
  than swapping files, e.g. sharing family videos.
• How can we support streaming applications in P2P
  networks?

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Streaming in P2P Networks

• Each peer might have limited resources
• Limited uplink bandwidth or processing power
• Several peers should collectively stream a
  requested content, this could achieve
• Higher available bandwidth, thus higher delivered
  quality
• Better load balancing among peers
• Less congestion across the network
• More robust to peer dynamics
• But streaming from multiple peers presents
  several challenges

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Challenges

• How to coordinate delivery among multiple
  senders?
• How to cope with unpredictable variations in
  available bandwidth?
• How to cope with dynamics of peer participation?
• Which subset of peers can provide max.
  throughput, thus max. quality?

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P2P Adaptive Layer Streaming (PALS)

• PALS is a receiver-driven framework for adaptive
  streaming from multiple senders
• All the machinery is implemented at receiver
• Using layered representation for streams
• Applicable to any multi-sender scenario

6
Justifying Main Design Choices

• Why receiver-driven?
• Receiver is a permanent member of the session
• Receiver has knowledge about delivered packets
• Receiver knows current subset of active senders
• Receiver observes each senders bandwidth
• Minimum load on senders gt more incentive!
• Minimum coordination overhead!
• Why layered representation for stream?
• Both MD and layered encoding should work
• Efficiency vs robustness
• PALS currently uses layered encoded stream

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Assumptions Goals

• Goals
• To maximize delivered quality
• Deliver max no of layers
• Minimize variations in quality

• Assumptions
• All peers/flows are cong. controlled
• Content is layered encoded
• All layers are CBR with the same BW
• All senders have all layers
• List of senders is given
• Not requirements

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Basic Framework

• Receiver periodically requests an ordered list
  of packets from each sender.
• Sender simply delivers requested packets with
  the given order at the CC rate
• Benefits of ordering the requested list
• Receiver can better control delivered packets
• Graceful degradation in quality when bw suddenly
  drops
• Periodic requests gt less network overhead

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Basic Framework

• Receiver keeps track of EWMA BW from each sender
• EWMA overall throughput
• estimate total no of pkts to be delivered during
  a period (K)
• Allocate K pkts among active layers (Quality
  Adaptation)
• Controlling bw0(t), bw1(t), ,
• Assign a subset of pkts to each sender (Packet
  assignment)
• Allocating each senders bw among active layers
• Keep senders sync. with receiver

Demux
bw (t)
bw (t)
bw (t)
2
1
3
bw (t)
0
buf
buf
buf
buf
1
2
3
0
C
C
C
C
Decoder
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Key Components of PALS

• Quality adaptation (QA) to determine quality of
  delivered stream, i.e. required packets for all
  layers during each interval
• Sliding Window (SW) to keep all senders
  synchronized with the receiver busy
• Packet Assignment (PA) to properly distribute
  required packets among senders
• Peer Selection (PS) to identify a subset of
  peers that provide maximum throughout
• We present sample mechanisms for QA, SW and PA.

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Quality Adaptation
ave bw
Draining phase
BW
str bw

• Same design philosophy as unicast QA but
  different approach
• Receiver-based
• Delay in the control loop
• Multiple senders
• Periodic adaptation, rather than on per-packet
  basis
• Two degrees of control
• Add/drop top layer(s)
• Adjust inter-layer bandwidth distribution

Filling phase
t
Demux
bw (t)
bw (t)
bw (t)
2
1
3
bw (t)
0
buf
buf
buf
buf
1
2
3
0
C
C
C
C
Decoder
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Quality Adaptation (contd)

• The goal is to control buffer state
• Total amount of buffered data
• Distribution of buffered data among active layers
• Controlling evolution of buffer state by
  regulating inter-layer bandwidth allocation (bw0,
  bw1, ..)
• Efficient buffer state
• min no of buffering layers with skewed
  distribution
• Efficient buffer state depends on the pattern of
  variations in bandwidth which is unknown at
  receiver
• Alternative solutions
• Use measurement to derive the pattern
• Assume a fix target buffer dist., e.g. buf(i)
  nbuf(i1)

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Quality Adaptation (contd)

• Run QA algorithm periodically, to plan for one
  period,
• Assume EWMA BW remains fix and estimate no of
  incoming packets
• Determine fill/drain phase, and no of layers to
  keep
• Filling Phase
• 1) Sequentially, fill up layers up to next target
  buffer state with n layers
• 2) Sequentially, fill up layers up to the target
  buffer state with n1 layers
• 3) Add a new layer, go to Step 1.
• Drain Phase
• 1) Sequentially, drain layers down to previous
  target buffer state
• 2) Drop the top layer, go to Step 1

Demux
bw (t)
bw (t)
bw (t)
2
1
3
bw (t)
0
buf
buf
buf
buf
1
2
3
0
C
C
C
C
Decoder
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Sliding Window

• Keep senders loosely synchronized with
  receivers playout time
• Sliding window, send a new request to each sender
  per window
• Overwriting, a new request overwrites old one

Period
D
Playout time
time
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Sliding Window (contd)

• Window size controls the tradeoff between
  responsiveness vs control overhead.
• Should be a function of RTT.
• Difficult to manage senders with major diff in
  RTT
• Keep senders busy when BW is underestimated.
• Reverse Flow Control (RFC) send a new request to
  a sender before it runs out of packets
• How should QA mechanism be coupled with the
  receivers requests to different senders?

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Coupling QA SW

• Synchronized Requesting
• Send new requests to all senders simultaneously
  when slides the window forward
• QA uses overall BW, rather than per-sender BW
• - fast sender becomes idle or overwriting slow
  senders
• - hard to manage senders with major difference in
  RTT.
• Asynchronous Requesting
• Send a new request to a sender when RFC signals
• Effectively manages different senders
  separately
• - Requires different approach to QA, i.e. QA
  should factor in individual BW

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Packet Assignment

• Given a pool of required packets in one interval,
  how to assign packets to senders?
• Number of assigned packets to a sender depends on
  its BW
• Need to cope with sudden decrease in BW
• Order each requested list based on importance of
  packets
• How to order all packets?
• How to divide them among senders?

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Packet Assignment (contd)

• Criteria for ordering packets, e.g. lower layers
  or lower playout time.
• Weighted round robin packet assignment

Ideal pattern
efficient pattern
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Preliminary Evaluation

• Synchronized requesting
• Configurations
• 3 PAL senders over RAP connections with diff RTT
• 10 TCP background flows
• window size 4 SRTT

spal2
5ms 10 Mb
10 TCP
spal0
spal1
15ms 10 Mb
20ms 5 Mb
25ms 10 Mb
spal0
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Scenario I
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Scenario II
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Related Work

• Streaming from multiple senders
• MD Apostolopoulos et al.
• CC streaming Nguyen et al.
• Streaming in P2P networks form distribution
  trees from peers
• CoopNet Padmanabhan et al.
• Existing systems, e.g. Abacast, chaincast,
• Structuring a distribution tree
• e.g. Zigzag Tran et al.
• Other receiver-driven adaptation,
• RLM McCanne et al.

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Conclusion Future Work

• PALS, a receiver-driven framework for streaming
  from multiple senders
• Receiver-based QA from multiple senders
• Keeping sender loosely synchronized
• Distributing load/packets among senders
• Future work
• Extensive simulation, to obtain deeper
  understanding of the dynamics in PALS
• Measurement-based derivation of BW variations
• Peer selection mechanism
• Effect of peer dynamics
• Supporting VBR streams and MD encoding

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PALS Peer-to-Peer Adaptive Layers Streaming

• Reza Rejaie
• CIS Department,
• University of Oregon
• Antonio Ortega
• Integrated Media Systems Center
• University of Southern California