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Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
QoE‐based Traffic Management for Multimedia Traffic in Mobile Networks
Dirk Staehle, DOCOMO Euro‐Labs
staehle@docomolab‐euro.com
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 2
• Motivation
• DOCOMO‘s QoE framework
• DASH: Dynamic Adaptive Streaming for HTTP
• DOCOMO‘s QoE framework and DASH– concept– results
• Conclusion
Outline
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 3
• UPCON: User Plane Congestion Control– 3GPP SA1 study item
• Scope (3GPP TR 22.805)– scenarios and use cases where high usage levels lead to user plane
traffic congestion in the RAN– make efficient use of available resources to increase the potential
number of active users while maintaining the user experience– handling of user plane traffic when RAN congestion occurs based on:
• the subscription of the user• the type of application• the type of content
3GPP Standardization: UPCON
Focus of QoE-based traffic management
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 4
• Traffic– total amount of traffic heavily increasing– web, data, video key traffic classes– video dominating
• Devices– increasing diversity – smartphones and notebook dominating
• Smart over‐the‐top applications– application adapt to performance provided by the network– Skype, DASH
Trends in Mobile Data Traffic
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 5
• Manage mobile web, data, ADAPTIVE and NON‐ADAPTIVE VIDEO traffic
• Traditional traffic management– differentiate traffic types: web, data, video– 20% interactive web ‐> high priority– 10% data e.g. software updates ‐> low priority– 70% video traffic ‐> no differentiation, one class
• QoE‐based traffic management for multimedia traffic– additionally differentiate based on video and device characteristics – optimize overall QoE for limited radio resources
Challenge
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Example VideosHigh
Data Ra
teLow Data Ra
te
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Application Sensitivity (QoE) Curves
Reference: Journal of Communications, 2009
Medium Data Rate
Low Data Rate
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 8
• Traffic Management Module (Optimization): decides on network resource allocation to provide best trade-off user satisfaction vs network cost– Input: network status, application sensitivity w.r.t. user satisfaction– Output: network resource allocation, feedback to application
RANCore Network
Contentsources
Traffic management(optimizer module)
Traffic engineering(adaptation, traffic shaper/
resource allocation, transcoding, layer dropping)
signaling
Mobileusers
Information gatheringApplication modeling
Network monitoring
QoE‐Based Traffic Management: Concept
• Traffic Engineering Module (enforcement function): adapt data stream to rate determined by optimizer to avoid uncontrolled QoE degradation (stalling, artifacts) due to packet loss or delays at eNB
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
RANCN
Contentsources
traffic management(optimizer)
Traffic engineering(adaptation, traffic shaper/
resource allocation)
Mobileusers
Network/RAN Monitoring(e.g. NW topology, device/link‘s status)
RNC/eNodeB
PGW
SGW
Applicationsensitivity
1. Data stream + Application sensitivity
3. Application sensitivity+ Network/RAN knowledge
4. Optimal rate adaptation(e.g. 300Kbps instead of
500Kbps)
Signaling
Data stream
5. Forward data stream totraffic engineering module+ signaling of optimal rate
adaptation 6. Return a modified data stream
500 Kbps
50
0 K
bp
s
30
0 K
bp
s
300 Kbps
7. Forward adapted data stream
2. Wireless resources/capacity
Cus
tom
er s
atis
fact
ion
Data rate (kbps)
SGSN
GGSN
xGSN EPC/
QoE‐Based Traffic Management: Architecture
8. Support rates for traffic subject to QoE Management
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
QoE‐Based Traffic Management and Adaptive HTTP Streaming
• How does end-to-end adaptive streaming solutions work together with network-centric QoE-based traffic optimization framework?
• Does end-to-end adaptation for Over-the-top (OTT) streaming services solve all congestion problems?
Results from cooperation with Prof. Steinbach, Lehrstuhl f. Medientechnik, TUMThanks to Ali El-Essaili and Damien Schröder for result figures
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 11
• Pull‐based HTTP streaming technology, client adapts multimedia quality to the rate experienced from the network
• Different proprietary solutionsMicrosoft’s Smooth Streaming, Apple’s HTTP Live Streaming, Adobe HTTP Dynamic Flash Streaming, …
• Standardized by MPEG and 3gpp: MPEG‐DASH, 3GP‐DASH
• Support by Microsoft, Adobe, …• Available clients: Microsoft Silverlight, VLC DASH client,…
Adaptive HTTP Streaming
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 12
• Progressive Download, Non‐adaptive HTTP Streaming
Non‐Adaptive HTTP Streaming
http request (video URL)
http response (video file)Video File
Video plays while downloading
Client
Server
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 13
Server
Dynamic Adaptive Streaming for HTTP
• Dynamic Adaptive Streaming over HTTP (DASH)
video segments in multiple representations
(formats of different quality and size)
MPD (Multimedia Presentation Description)‐ defines in what format and where the video is stored‐ URLs of video segments of different quality
request for MPDMPD
request for segment 1, representation xsegment k, representation y
request for segment k, representation ysegment k, representation y
Video File
MPDClient
while playing client determines best
representation based on available rate and playout
buffer
First 2s in high quality
Seconds 4-6 in low quality
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 14
• Features of QoE Optimization Framework– traffic management (optimizer): determine optimal video quality and
data rates taking into account video characteristics (QoE curve) and long‐term radio channel quality
– traffic engineering (enforcement function): adapt video to rate determined by optimizer to avoid uncontrolled QoE degradation (stalling, artifacts) due to packet loss or delays at eNB
• Features of DASH– adapt video to available bandwidth to avoid uncontrolled QoE
degradation (stalling)
DASH offers lightweight traffic engineering (enforcement) functionality– reactive approach– proactive approach
DASH and QoE‐Based Traffic Management
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
P-GW
DASH Client
UE
Reactive Approach
eNB
HTTP or Streaming
ServerTE
DASH Proxy
Rate Shaper
HTTP Requests
HTTP Response(Video Segments)
Video RateMPD,
Segment Requests
Optimizer
TMLong‐term channel quality
Video Information(QoE value per representation)
Standard DASH client adapts
requests for rate set by rate shaper
Rate Shaper limits data rate of HTTP download to target
rate set by optimizer
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
P-GW
DASH Client
UE
Proactive Approach
eNB
HTTP or Streaming
ServerTE
DASH Proxy
Rate Shaper
HTTP Requests
HTTP Response(Video Segments)
Video RateMPD,
Segment Requests
Optimizer
TMLong‐term channel quality
Video Information(QoE value per representation)
Standard DASH client adapts
requests for rate set by rate shaper
Representation
DASH proxy modifies URL of requested segment to
URL of optimal representation
Rate Shaper limits data rate of HTTP download to target rate set by
optimizer
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Evaluation Framework
DASH Client
UE
HTTP or Streaming
Server
Proxy
Rate Shaper
HTTP Requests
HTTP Response(MPD, Video Segments)
Microsoft Silverlight,VLC DASH ApacheSquid Proxy Dummynet
Live
Channel Traces QoE CurvesScheduler / OptimizerSimulation
representation per segment
data rate per 2ms interval
Offline
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 18
• 8 different videos• 50 replications with different channel traces• Evaluation of
– video specific MOS (averaged over segments) – mean MOS (averaged over segments and videos)
• Comparison of– pure DASH (Non‐Opt)– reactive approach, only rate shaper (QoE reactive)– proactive approach
• rate shaper + proxy, opt. on continuous QoE curves (QoE‐Proxy)• rate shaper + proxy, opt. on discrete QoE points sets (QoE‐Proxy‐d)
– server based approach, optimal encoding at server (QoE‐Server)
Scenario
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Mean MOS
Potential Gain of 0.5 in Mean MOS by QoE framework
Gain of 0.25 in Mean MOS by proactive approach
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Individual MOS
Almost equal Mean MOS for non-demanding videos
Clear Mean MOS improvement for demanding videos
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 21
Subjective Tests
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
Overall results
30 km/h 120 km/h
Mean MOS improves
MOS range gets smaller
(“fairer”)
Proactive Reactive DASH Proactive Reactive DASH
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group 23
• Trends in mobile multimedia communication– video is/becomes dominating traffic type– smart applications like adaptive HTTP streaming
• QoE based traffic management framework– differentiates multimedia content based on its inherent characteristics– efficiently utilizes radio resources for an overall optimal QoE– flexible to integrate new streaming technologies
• End‐to‐end adaptive streaming (DASH)– is a big step for high‐quality mobile multimedia delivery but – rate allocation depends on eNB scheduler such that – QoE‐based traffic management achieves significant gain, in particular
for demanding videos
Conclusion
Copyright © 2012 DOCOMO Communications Laboratories Europe GmbH Dirk Staehle, Service Research Group
DOCOMO Communications Laboratories Europe GmbHLandsberger Strasse 312 – 80687 Munich, GermanyPhone: +49 (89) 56824‐0 | www.docomolab‐euro.com
Dr. Dirk Staehlestaehle@docomolab‐euro.com