improving qos and resource utilization for multimedia ... improving qos and resource utilization for...
TRANSCRIPT
1
Improving QoS and Resource Utilization for Multimedia Streaming over 3G
Kamal Deep Singh
Co-advisors : David Ros and Laurent ToutainThesis Director : César Viho
2
3G/UMTS Wireless Network
Users of Voice, TCP, …
Video Streaming anywhere in the cell
“Hotspots” with good channel conditions
3
Contributions and Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network
How to improve QoS & resource utilization ?
How can applications adapt to improve the multimedia quality ?
User
4
Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network User
5
Core Network
Overview: 3G/UMTS Network
Radio Access Network
6
Overview: 3G/UMTS Problems
Problems due to the use of IPIP doesn’t support real time streaming requirementsOverhead due to packet header
Problems due to radio conditionsScarce and time varying bandwidthCongestion, wireless losses & large delay
7
Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network User
8
Video Streaming
Video Streaming
• Delay & Jitter• Bandwidth• Packet loss
constraints
9
Video Streaming …
Video Streaming
• Underflow• No Playout
• Packet losses will cause quality distortion
10
Video Streaming …
IP doesn’t support video streaming requirements
Solution is to provide QoS in UMTS network
Different priorities to different flowsService guarantees, …
11
UMTS Downlink High Speed Downlink Packet Access (HSDPA)
Enhanced bit rates: 14.4MbpsFast and adaptive packet scheduling
Packet scheduler looks at a term: channel condition, …
Determines which user will be scheduledThe chances of a user being scheduled
12
Existing HSDPA Schedulers
MAX CI (shown in previous slide)Looks at instantaneous data rate : Unfair
Proportionally Fair (PF)Looks at the ratio of:
QoS SchedulersLook at a “QoS function” and the above ratio
To satisfy QoS constraints or guarantees
Instantaneous data rate
Average throughput
* proportional to channel conditions
*
13
Existing HSDPA Schedulers …
QoS schedulers …QoS function based on the Delta
(Throughput guarantee - Average throughput)
Guarantee not satisfied:Value of QoS function increasesIncreases the probability of being scheduled
Guarantee satisfied:The probability of being scheduled doesn’t increase To be fair to other users
14
Existing HSDPA Schedulers …
How does a QoS function look like ?Rate Guarantee (RG) [Hosein 2002]:
α i.e. a constant in another variant [Lundevall 04]
Bi(t) =
(1 + λi(t) · β · exp (−β · (λi(t)− λ
(i)min)) ∀i ∈ QoS,
1 ∀i ∈ BE,
Guaranteed RateAverage Throughput
Best Effort (BE) users without QoS constraints
15
Existing QoS Schedulers …
RG and its variant have the following problems:
Increase in Best Effort (BE) users deteriorate the QoS
Bias towards certain Rate Guarantees
16
Normalized Rate Guarantee (NRG) Scheduler
Based on the theory in [Hosein 2002] we propose:
Bi(t) =
⎧⎨⎩λ(i)min + λi(t)β · exp
µ−β ·
(λi(t)−λ(i)min)
λ(i)min
¶∀i ∈ QoS,
kBEnBE
∀i ∈ BE.
Normalized with number of BE users
Normalized with Rate Guarantee
Resources allocated to the BE users
17
Simulation Platform
UMTS/HSDPA Simulation Platform
EURANE (NS-2 Extension)
Detailed implementation
SNR traces to simulate physical layer
18
Simulation Platform …H.264 video flows • UM mode
• Per user queues• DiffServ AQM
Pedestrian A, 3km/h
• HSDPA Packet Schedulers• Max. cell capacity: 3.6Mbps
Background TCP flows:• Long lived• WWW
Cell radius: 500m
19
How to evaluate video quality?
Objective evaluationPSNR (peak signal to noise ratio)Pixel by pixel comparison
Subjective evaluationEvaluation done by real humans
20
How to evaluate video quality? …
The importance of subjective quality evaluation:
5/5
Excellent
1/5
Very annoying
3/5
Fair
Scores:
21
Pseudo-Subjective Quality Assessment (PSQA) [Rubino et al. 2002]
Set of Network Parameters
Training
Real Time Usage
Mean Opinion Score
Real Network
RNN
Trained RNN
22
Results4 video users (384kbps, H.264) Qmin: Minimum PSQA score after removing lower 5%NRG performs better for higher loads
Video Users TCP Users
23
Results …
Mix of TCP and WWW trafficNRG performs better for higher loads
Video Users WWW Users
24
Results …Loss Rates for different Rate GuaranteesFor fair comparison
Normalized Best Effort term of RG and RG Lundevall variant
25
Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network User
26
Header Compression for Audio Flows
AudioData
IP/UDP/RTP40 – 120 bytes
100 bytes
AudioData
Compressed Header
2 – 5 bytesSignificant Overhead
Removes redundant information like IP src, dst …
Robust Header Compression (ROHC)
27
ROHC Study: Results
Tradeoff:Compression efficiency Robustness
Propose dynamic negotiationUpdate parameters: error rate, …Efficient when low error Robust when high error
Result: Overall performance improved Lower loss rateBetter compression efficiency
28
ROHC Study: Results …UDP checksum: Corrupted payload droppedCorrupted data may be useful!Studied (UDP-Lite + ROHC) vs. (UDP + ROHC)
ROHC performance doesn't changeProposed CRC strategies
UDP
CRC 50%
CRC 25%
CRC 0%
UDP-Lite
29
Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network User
30
Congestion Control for Video Flows
Motivation• Variable bandwidth, delay …• Congestion, packet losses
How can applications adapt to the network conditions?
31
Existing Schemes
Congestion control for videoTCP: Retransmissions, rate oscillations, …
TFRC [Floyd 2000]Sending Rate is calculated by a TCP model
Better Rate Stability
TCP Friendly
32
Simulation StudyProblem: Rate stabilityRadio conditions TFRC rate variance similar to TCP Improvement with Rate Guarantee (RG) scheduler
33
More Problems: Wireless Losses
Problem: Two types of losses in wireless Networks
Packet drops due to congestionPacket drops due to bad channel conditions
Node B
Router
Wireless loss
Wireless network
IP Packets IP Queue
Congestion loss
34
More Problems …
Inefficiency for TCP, TFRC …Cannot distinguish between these losses.Reduce their sending rate on loss.
How to distinguish Wireless losses from congestion losses?
Previous Work have used Round Trip Time variations
Not reliable
35
Differential dropping in the DiffServ (Green, Yellow & Red)
Video applications mark their packets
Wireless Loss Estimation (Background: DiffServ)
Drop Red packets
Drop Red + Yellow packets
Rarely drop Green packets
I B P B P
Increasing Congestion
36
Wireless Loss Estimation
Wireless Loss Estimation in DiffServ (WLED) Networks:
Red packets are dropped first on congestion
Wireless loss rate (w) is correlated with green loss rate
If loss of yellow packets is not significant
37
WLED:Improves link utilization
There is no change in other properties: TCP friendliness, loss rate, rate stability
But, works only with DiffServ aware applications
WLED: Results
WLED
TFRC
TCP
38
Congestion Control and Adaptive Retransmissions
We improved link utilization in case of wireless losses.
But, lost data still deteriorates the quality!
Solution: We integrate a scheme to retransmit the lost data.
39
Congestion Control and Adaptive Retransmissions …
Retransmission SchemeIf packet has the possibility to arrive before its deadline
No congestionEnables retransmission schemes
CongestionDisable retransmission
Depending on BW retransmit eitherI framesI + P frames orAll frames
WLED scheme integrated
40
Congestion Control and Adaptive Retransmissions: Results
• 10 WWW users in background
41
Agenda
• Overview
• Video Streaming over 3G
• Header Compression
• Congestion Control for Video Flows
• Conclusion and Future Work
Network User
42
Conclusion
Studied QoS scheduling for video streaming over 3G
Subjective quality evaluation
Proposed a new QoS schedulerBetter quality even with high background loadNot biased
Improved header compression performanceUpdating channel states Lower loss rate with variable UDP-Lite checksum coverage
43
Conclusion …
Studied congestion control for video flowsTFRC rate stability not better than TCP as reported in previous works
Proposed a wireless loss estimation scheme
Improved the link utilization
Proposed a retransmission schemeImproved video quality by recovering lost data
44
Results published in following Papers
Kamal Deep Singh and David Ros. ``Normalized Rate Guarantee Schedulerfor High Speed Downlink Packet Access''. In IEEE GLOBECOM 2007, Washington, November 2007.
Kamal Deep Singh, David Ros, Laurent Toutain and César Viho. ``Proportional Resource Partitioning over Shared Wireless Links''. In IEEE 66th VTC, Baltimore, MD, USA, September 2007.
Kamal Deep Singh and David Ros. ``TCP-Friendly Rate Control over High Speed Downlink Packet Access''. In IEEE ISCC'07, Aveiro, Portugal, July 2007.
Kamal Deep Singh, Julio Orozco, David Ros and Gerardo Rubino. ``Streaming of H.264 Video over HSDPA: Impact of MAC-Layer Schedulerson User-Perceived Quality''. In ENST Bretagne Research Report no. RR-2007002-RSM, 2007.
Kamal Deep Singh, David Ros, Laurent Toutain and César Viho. ``Improving Multimedia Streaming over Wireless using End-to-End Estimation of Wireless Losses''. In IEEE 64th VTC, Montreal, Canada, September 2006.
45
Results published in following Papers …
Ana Minaburo, Kamal Deep Singh, Laurent Toutain, Cécile Marc and Elizabeth Martinez. ``Performance Improvement of Multimedia flows by using UDP-Lite and ROHC compression''. In 5th ICICS, Bangkok, Thailand, December 2005.
Ramiro Garcia, Jose Incera and Kamal Deep Singh. ``An Experimental evaluation ofH.264/AVC semantic codec video streaming in a Diffserv network architechture''. In ROC&C 2005, Acapulco, Mexico, November 2005.
Pablo Frank Bolton, Jose Incera and Kamal Deep Singh. ``Objective and subjective characterization of video streams in IP networks''. In ROC&C 2005, Acapulco, Mexico, November 2005.
Ana Minaburo, Kamal Deep Singh, Laurent Toutain and Loutfi Nuaymi. ``Proposedbehavior for robust header compression over a radio link''. In ICC 2004, Paris, France, June 2004.
Submitted
Kamal Deep Singh, Arpad Huszak, David Ros, César Viho and Gabor Jeney. ``Congestion Control and Adaptive Retransmission for Multimedia Streaming overWireless Networks''. Submitted to ICC 2008.
Kamal Deep Singh, Julio Orozco, David Ros and Gerardo Rubino. ``Streaming of H.264 Video over HSDPA: Impact of MAC-Layer Schedulers on User-Perceived Quality''. Submitted to IEEE Transactions.
46
Future Work
Call admission control and management for HSDPA
Real time PSQA feedbackDynamic rate guarantee negotiation
Improve the rate stability of TFRC over HSDPALook at the performance of WLED + retransmission in real networkCongestion control for MPEG4-scalable video codec (SVC)
47
Thank You !
48
Appendix
49
High Speed Downlink Packet Access (HSDPA) Max CI scheduler chooses a user such that :
Instantaneous Data Rate*
*Note that instantaneous data rate is related to the received power
i∗
i∗ = argmaxi{Ri(t)}
50
Existing HSDPA schedulers
Proportionally Fair :
i∗ = argmaxi
½Ri(t)
λi(t)
¾Average Throughput
i∗ = argmaxi
½Bi(t)
Ri(t)
λi(t)
¾Barrier Function
i∗ = argmaxi
½Bi(t)
Ri(t)
λi(t)
¾Barrier Function
i∗ = argmaxi
½Bi(t)
Ri(t)
λi(t)
¾Barrier FunctionQoS schedulers :
51
Existing QoS schedulers
[Hosein 2002] QoS schedulers can be designed based on
Rate Guarantee (RG) [Hosein 2002] uses :
U(λ)
i∗ = argmaxi{Ri(t) · U
0
i (λi(t))}.
U(λ) = log(λ) + 1− exp (−β · (λ− λmin))
Guaranteed Rate
52
Existing QoS schedulers …
RG uses the barrier function :
α i.e. a constant in another variant [Lundevall 04]
Bi(t) =
(1 + λi(t) · β · exp (−β · (λi(t) − λ
(i)min)) ∀i ∈ QoS,
1 ∀i ∈ BE,
Guaranteed Rate
53
Normalized Rate Guarantee (NRG) Scheduling
We propose NRG with the following user utility :
Normalized with number of BE users
Normalized with Rate Guarantee
Resources allocated to the BE users
UQoS(λ) = λmin ·
µlog (λ) +1− exp
µ−β ·
λ−λminλmin
¶¶,
UBE(λ) =kBEnBE
log(λ).
54
Results
4 video users (384kbps, H.264) & 10 TCP long flowsSome users may not get good qualityBetter to drop those users instead of wasting BW
55
Congestion control and Adaptive Retransmissions : Results …
• Wireless loss probability 0.1 and varying WWW users