Transcript overlay routing tables

Optimizing User QoE through Overlay
Routing, Bandwidth Management and
Dynamic Transcoding
Maarten Wijnants, Wim Lamotte
Hasselt University - Expertise Centre for Digital Media
Bart De Vleeschauwer, Filip De Turck, Bart Dhoedt, Piet
Demeester
Ghent University – IBCN - Department of Information Technology
Peter Lambert, Dieter Van de Walle, Jan De Cock, Stijn
Notebaert, Rik Van de Walle
Ghent University – MMLab - Department of Electronics and Information Systems
Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding
Outline
• Introduction and Motivation
• End-to-End QoE Optimization Architecture
– Overlay Routing Components
– Network Intelligence Proxy
• H.264/AVC Video Transcoding
• Evaluation
– Experimental Setup
– Experimental Results
– Discussion
• Conclusions
Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding
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Introduction and Motivation
• Rising networked access of MM services
– Strict requirements on transportation network
• Service consumption environment has
become highly heterogeneous
– Growing service dependability & adaptation
requirements
• Current-gen networks often not capable of
guaranteeing requirements are satisfied
– Internet routing service is best-effort
– Constrained access network connections
• Insufficient last mile bandwidth  Congestion
Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding
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Introduction and Motivation
• Current networks often unable to provide
MM users an acceptable usage experience
– More formally: Quality of Experience (QoE)
• Network architecture supporting full endto-end QoE optimization needed
– Proposed by us in previous work
• We extended network architecture with a
H.264/AVC video transcoding service
– Dynamic rate adaptation of H.264/AVC video
– Enables further optimization of user QoE
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End-to-End QoE
Optimization Architecture
• Proposed architecture employs 2-tier
approach to achieve E2E QoE optimization
– Enhance data dissemination in network core
• Through provision resilient overlay routing service
– Last mile user QoE optimization
• Network traffic shaping
• Multimedia service provision
• Consists of 3 types of components
– Overlay Server
– Overlay Access Component
– Network Intelligence Proxy
Resilient overlay
routing
Last mile QoE
optimization
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End-to-End QoE
Optimization Architecture
• Overlay Server (OS)
– Deployed in network core
– Maintain an overlay topology
• Perform active monitoring to obtain connectivity info
• Info is used to construct overlay routing tables
• Overlay Access Component (AC)
– Located near end-users
– Decide when to forward traffic to overlay
servers (based on quality direct IP connection)
• OSs exploit overlay routing tables to
transport traffic to AC close to target node
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End-to-End QoE
Optimization Architecture
• Network Intelligence Proxy (NIProxy)
– Deployed close to end-user
– Improve user QoE by intelligently managing
last mile content delivery to clients
– Context introduction in transportation network
• Network awareness: Access channel conditions
• Application awareness: E.g. stream significance
– Last mile network traffic shaping: Orchestrate
last mile BW consumption of applications
• Prevent over-encumbrance of client's access link
• Intelligently allocate available client downstream BW
(based on application awareness)
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End-to-End QoE
Optimization Architecture
• Network Intelligence Proxy
– Network traffic shaping operates by organizing
network flows in a stream hierarchy
• Internal nodes: Implement BW distribution technique
– E.g. WeightStream
• Leaf nodes: Correspond to actual network flows
– Discrete: Toggle between discrete # of BW values
– Continuous: Any rate in [0, max flow BW usage]
– Multimedia service provision
• Perform computation/processing on network flows
• Services can query and exploit NIProxy’s awareness
• Implementation: Plug-in approach (dynamic loading)
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End-to-End QoE
Optimization Architecture
Resilient network
core routing
Overlay
layer
Network
layer
Last mile QoE optimization
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H.264/AVC Video
Transcoding
• Focus on bit rate reduction
• Operates entirely in compressed domain
– Only entropy decoding and encoding required
– # transformed coefficients are set to 0 based
on dynamically changing cut-off frequency
– Transcoder steered by rate control alg
• Ensures desired bit rate is achieved (Track buffer
occupancy  Estimate bit budget current frame 
Dynamically adjust cut-off frequency)
• Integrated as plug-in for NIProxy
– Dynamically set desired bit rate H.264 flows
• Enables H.264 flow mgmnt using continuous leaves
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Evaluation
Experimental Setup
• Experimental results produced on testbed
– 10 Linux PCs: 3 OSs, 2 ACs, 2 NIProxies, 2 MM
clients, video server, 2 Click impairment nodes
– Click nodes emulate varying network condition
• Introduce random packet loss in core network
• Enforce BW restriction on last mile
– Communication session server to each client
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Evaluation
Experimental Results
• Experiment
– 2 H.264/AVC flows
streamed to each client
– Consisted of 5 intervals
– Bit rates continuous
leaf nodes enforced by
H.264/AVC transcoder
Interval 5:
1:+Additional
2:
3
Only
Introduction
4: Significance
1
last
V2;
V1
increased
identical
V1
is
H.264/AVC
mile
and
BWV2
available;
had
flow;
sufficient
used
to
allocated
more
BW available
weight
upgrade
and
quality
comparable
toBW
forward
V2 
(V1V2
max
bitmaximal
rate
received
flow at
transcoded
already
at
maximal
tolower
quality
quality)
bit rate
comparable BW budget
Continuous
leaf nodes
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Evaluation
Discussion
• Findings
– Client’s last mile downstream capacity
respected  Last mile congestion avoided
• Outcome = Optimal flow reception at client-side
– BW distribution captured stream importance
• Due to NIProxy’s application awareness
– H.264/AVC transcoding service enabled
continuous video adaptation
• Optimal and full exploitation available last mile BW
• Did not apply for the “unprotected” client!
– Degraded video playback at client-side
– Clear difference in QoE provided to both clients!
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Conclusions
• E2E QoE optimization platform
– Resilient overlay routing service circumvents
erratic parts of network core
– Last mile QoE optimization through bandwidth
management and multimedia service provision
• Extended with H.264/AVC transcoding
– Enables continuous video adaptation
• Experimental results demonstrate positive
impact on QoE optimization capabilities
– Full exploitation available last mile BW
– More dynamic and effective BW distributions
Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding
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Thank you for your attention!
Any questions?
Optimizing User QoE through Overlay Routing, Bandwidth Management and Dynamic Transcoding