28th Symposium On Applied Computing
Dependable and Adaptable Distributed Systems Track
Improving Context Interpretation by
Using Fuzzy Policies:
The Case of Adaptive Video Streaming
Lucas Provensi, Frank Eliassen, Roman Vitenberg and Romain Rouvoy
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Adaptive Video Streaming
• Increasing number of application exploiting media
streaming over the Internet:
• Video conferencing, video on demand, etc.
• Applications need to adapt to dynamic
environments:
• Shared bottlenecks on the Internet
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Challenges of Adaptation: Information
Imperfection
Video
Capturing
Application
Raw Video
File
network
H264
Encoding
RTSP
Server
RTP Stream
RTCP reports
RTSP
Client
H264
Decoding
Video
Rendering
Application
Save to File
Application requirements regarding video quality
Encoder characteristics and parameters
Signaling, streaming and feedback mechanisms
Shared link dynamics
SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
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Existing Approaches
• Assume precise information
• Bitrate overshoots and frequent quality oscillations
• Examples: Increase/decrease protocols and TFRC
Original video sequence
Adapted using TCP-Friendly Rate Control
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Proposed Solution
• Development approach for adaptive systems
that:
• Captures imprecision by using fuzzy set theory
• Integrates a fuzzy inference engine into a modular
MAPE-K adaptation loop
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Proposed Solution
• Why fuzzy sets?
• Example: “Reduce bitrate when packet loss fraction is high”
Degree of confidence
1
acceptable
high
low
0
10%
2%
Packet Loss Fraction
SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
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Proposed Solution: MAPE-K loop
H264
Encoding
Video Stream
RTSP Client
RTSP Server
RTP reports
Actuator
Sensor
RTCP reports
Knowledge Base
Execute
Domain specific
ontologies
Monitor
Fuzzy Adaptation
Policies
Plan
events
Analyze
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Proposed Solution: Knowledge Specification
Video Stream
Domain specific ontologyH264
RTSP Server
Encoding
Event
Actuator
Loss
Event
RTP reports
Knowledge Base
Loss
Fraction
Execute
Low L.F.
Acceptable
L.F.
sub-concept
property
Plan
Domain specific
ontologies
Fuzzy Adaptation
High L.F.
Policies
fuzzy predicate
Sensor
RULE
RULE 11: :
IF loss_fraction
IF loss_fraction
IS low ANDISrttlow
IS NOT high
adjustment
IS positive
THEN THEN
adjustment
IS positive WITH
0.4;
RULE
Monitor2 :
IF loss_fraction IS high
THEN adjustment IS negative
RULE 3 :
IF loss_fraction IS acceptable
THEN adjustment IS null
Analyze
RULE 4 :
IF accumulated_loss IS negative AND rtt IS NOT
high THEN adjustment IS positive WITH 0.2;
acceptable
low
RTSP Client
Adaptation
Policies
high
SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
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Proposed Solution: Analysis
H264
Encoding
acceptable
Video Stream
RTSP Server
Actuator
Degree
reports
ofRTP
truth
RTSP Client
Sensor
RULE 2 :
IF loss_fraction IS high
THEN adjustment IS negative
Knowledge Base 0.1
0.4
low
0.1
Execute
Measured
Loss fraction
Rule
high
RULE 3 :
Monitor
IF loss_fraction IS acceptable
THEN adjustment IS null
0.4
Analyze
Plan
Accumulation
Fuzzy values for
all possible
actions
Fuzzification
Rule Evaluation
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Proposed Solution: Planning
H264
Encoding
RTSP Server
Video Stream
Rate
0.4
Execute
Knowledge Base
Sensor
null
negative
Monitor
0.1
-1
Adjustment
crisp value
Plan
Defuzzification
RTSP Client
RTP reports
Actuator
Encoder
adjustment
Adjustment
-0.2
0
positive
Center of mass
1
Analyze
Fuzzy values for
all possible
actions
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Evaluation: Network Level Simulations
Fuzzy rate adaptation achieves better throughput, less oscillations and
acceptable packet loss fraction
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Evaluation: Video Quality
Video Quality Metric
stSSIM (higher is better)
Scenario 1
Scenario 2
Scenario 3
Fuzzy TFRC
Fuzzy
TFRC
Fuzzy
TFRC
0.53
0.61
0.49
0.34
0.26
0.32
Fuzzy Rate Adapt.
TFRC
Better overall video quality according to objective metrics
SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
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Conclusion
• Problem
• Adaptation in presence of imprecise information
• Our approach
• Separation of concerns
• Example: separate adaptation knowledge from its
interpretation
• Integrate a fuzzy inference engine into the adaptation
loop
• Validated for the case of adaptive video streaming
Questions?
SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Evaluation (extra)
• Definition of a complete set of adaptation rules
implementing a PID bitrate controller
• ns-2 simulations
• Static topologies (dumbbell and parking lot)
• Varying number of competing flows (tcp and udp)
• Performance comparison with same application using
TFRC protocol to determine the target bitrate
• Video quality evaluation
• Metrics:
•
•
•
•
Peak Signal-to-Noise Ratio (PSNR),
Structural Similarity Index (SSIM),
Spatial-Temporal SSIM (stSSIM),
DCT based Video Quality Metric (VQM)
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Evaluation (extra 2)
• Increasing number of tcp flows
• On/off traffic traversing a increasing number of
intermediate routers
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track
Evaluation (extra 3)
• Media Quality Evaluation
• Scenario 1: No competition;
• Scenario 2: Competing with a TCP flow (new reno)
and a fixed bitrate UDP flow.
• Scenario 3: Competing with 10 TCP flows
Metric
Scenario 1
Scenario 2
Scenario 3
Fuzzy TFRC
Fuzzy
Fuzzy
TFRC
TFRC
PSNR (higher is better)
33.07 29.77 35.02
32.77 26.17
25.24
SSIM (higher is better)
0.91
0.82
0.97
0.89
0.76
0.78
stSSIM (higher is better)
0.53
0.32
0.61
0.49
0.34
0.26
VQM (lower is better)
1.69
2.20
1.52
1.58
2.31
2.53
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SAC´13 March 20, 2013, Coimbra, Portugal – DADS Track