SlideShare ist ein Scribd-Unternehmen logo
1 von 16
Downloaden Sie, um offline zu lesen
Media‐Aware
Network
Elements

                     on
Legacy
Devices


                                           m16695


                Ingo
Kofler,
Robert
Kuschnig,
and
Hermann
Hellwagner

                                 Chris:an
Timmerer



                 Klagenfurt
University
(UNIKLU)

Faculty
of
Technical
Sciences
(TEWI)

          Department
of
Informa:on
Technology
(ITEC)

Mul:media
Communica:on
(MMC)

 h;p://research.@mmerer.com

h;p://blog.@mmerer.com

mailto:chris@an.@mmerer@itec.uni‐klu.ac.at



Acknowledgement:
Part
of
this
work
is
supported
by
the
European
Commission
in
the

 context
of
the
and
ENTHRONE
(contract
no.
038463)
project.
Further
informa@on
is

                      available
at
h;p://www.ist‐enthrone.org.


Outline

•  Mo@va@on
and
Introduc@on

•  List
of
Technologies


•  Architecture
and
Performance
Evalua@ons


•  Demo
Video


•  Conclusions
/
References



2009/07/01
      Chris@an
Timmerer,
Klagenfurt
University,
Austria
   2

Mo@va@on
and
Introduc@on

•  Adapta@on
of
an
SVC
bitstream

      –  Achieved
by
removing
certain
NALUs
➙
filtering
of
NALUs

      –  Steered
by
a
(TID,
DID,
QID)
tuple
➙
filter
criteria

      –  Computa@onally
cheap
(compared
to
transcoding
etc.)


•  Idea

      –  Perform
real‐@me
in‐network
adapta@on
of
the
SVC
bitstream

         on
an
ordinary,
low‐cost
WiFi
router

      –  Media‐aware
Network
Element
(MANE)


•  Applica@ons
of
in‐network
adapta@on

      –  Cross‐layer
adapta@on
on
the
access
point

      –  Adapta@on
for
different
end‐devices

2009/07/01
            Chris@an
Timmerer,
Klagenfurt
University,
Austria
   3

Media‐aware
Network
Element





              On‐the‐fly
adapta@on
of
scalable
coded
video
content
in
a
media‐
                             aware
network
element
(MANE).




                                                 Source:
h;p://ip.hhi.de/imagecom_G1/savce/

2009/07/01
                 Chris@an
Timmerer,
Klagenfurt
University,
Austria
            4

List
of
Technologies

•  Scalable
Video
Coding
(SVC)

•  Real‐@me
Streaming
Protocol
(RTSP)

      –  Establishing
and
controlling
the
streaming
session

      –  VCR‐like
control
of
the
streaming
(Start,
Stop,
Pause)

•  Real‐@me
Transport
Protocol
(RTP)

      –  Encapsulates
the
video
and/or
audio
content

      –  Mostly
used
on
top
of
the
unreliable
UDP
protocol

      –  Offers
sequence
number,
@mestamps
for
syncing
the

         playback

      –  Generic
header
with
content‐specific
payload
format

         (AVC,
SVC,
…)


2009/07/01
           Chris@an
Timmerer,
Klagenfurt
University,
Austria
   5

Proxy Approach
              server
                router
with
proxy

                                          router
                                           client


      RTSP
      RTP/RTCP
             RTSP
      RTP/RTCP
                         RTSP
     RTP/RTCP


      TCP
            UDP
              TCP
           UDP
                         TCP
           UDP


                IP
                              IP
                                         IP


          MAC/PHY
                          MAC/PHY
                                    MAC/PHY



      •  Proxy on network device between client & server
              –  Intercepts the RTSP / RTP communication
              –  Proxy is transparent for the client
              –  Acts as client for initial server and as server for the client
      •  Implications
              –  Proxy has to modify parts of the request (e.g. port numbers)
              –  Proxy can then adapt the SVC video stream carried over RTP
2009/07/01
                    Chris@an
Timmerer,
Klagenfurt
University,
Austria
                         6

Proxy Approach (cont’d)

Benefits
                                       Drawbacks

•  Session‐
and
stream‐/                       •  Proxy
is
running
as
user‐
   media‐awareness

                              space
process


•  Enables
stateful
inspec@on
                 •  RTP
packets
have
to
be

   and
processing
of
packets
                     passed
(copied)
from
the

•  Allows
adapta@on
on
a
per‐                     kernel‐space
to
the
user‐
   session
basis
                                 space
and
vice
versa

•  Consistent
RTCP
receiver
                   •  Decreases
theore@cal

   and
sender
reports
                            throughput
compared
to

                                                  kernel‐internal
solu@on
(e.g.

                                                  filtering
on
IP
level)


2009/07/01
         Chris@an
Timmerer,
Klagenfurt
University,
Austria
        7

Architecture

                                                    Handles
incoming
RTSP
requests
(554

                                                    redirect
to
proxy
using
iptables)


                                                    SDP
stored
in
LRU‐based
cache
un@l

                                                    session
established


                                                    Maintains
state:
seq#,
@mestamps,

                                                    SVC
params,
monitoring,
etc.


                                                    Forwards
non‐SVC
packets

                                                    Adapts
SVC
packets


                                                    Acts
as
server
for
client

                                                    Acts
as
client
for
server


                                                    Retrieve
monitoring
informa@on

                                                    Modify
adapta@on/SVC
parameters


2009/07/01
   Chris@an
Timmerer,
Klagenfurt
University,
Austria
                    8

Performance
Evalua@ons

•  Hardware:
Linksys
WRT
54
GL

      –  Broadcom
System‐on‐Chip
BCM5352EL

      –  MIPS32
200
MHz
CPU

      –  16
MB
RAM

      –  4
MB
Flash
Memory

      –  IEEE
802.11b/g
WLAN

      –  Fast
Ethernet
switch
with
5
ports

      –  price
~
45
Euros

(May
2008)

•  Sonware:
OpenWrt

      –  Linux‐based
firmware
for
Broadcom‐based
WiFi
routers

      –  gcc-based SDK for OpenWrt available
      –  Proxy
implementa@on
in
ANSI
C

2009/07/01
          Chris@an
Timmerer,
Klagenfurt
University,
Austria
   9

Performance
Evalua@ons
(cont’d)

•  Evalua@on
of
the
implementa@on
on
the
target
plaporm

      –  Server
–
proxy
–
client
deployment

      –  Inves@ga@on
of
worst‐case
scenario
(no
adapta@on)


•  Performance
metrics

      –  CPU
usage
for
different
number
of
streams

      –  Delay
introduced
by
the
proxy
on
a
complete
access
unit
(frame)


•  Three
different
SVC
streams
for
evalua@on

      –  Foreman,
CIF,
30
Hz,
715
kbps

      –  City,
4CIF,
30
Hz,
1247
kbps

      –  Harbour,
4CIF,
30
Hz,
2056
kbps


2009/07/01
             Chris@an
Timmerer,
Klagenfurt
University,
Austria
   10

Performance
Evalua@ons
(cont’d)

CPU
Usage





2009/07/01
    Chris@an
Timmerer,
Klagenfurt
University,
Austria
   11

Performance
Evalua@ons
(cont’d)

CDF of delay for sequence city (1245 kbps)





2009/07/01
      Chris@an
Timmerer,
Klagenfurt
University,
Austria
   12

Demo
Video





2009/07/01
   Chris@an
Timmerer,
Klagenfurt
University,
Austria
   13

Conclusions

•  Proxy
approach
for
in‐network
adapta@on
on
a
per‐packet

   basis

      –  Aware
of
sessions
and
individual
streams
(media‐aware)

      –  Stateful,
not
a
simple
packet
dropper

•  Applica@ons

      –  Adapta@on
according
to
device
capabili@es

      –  Cross‐layer
adapta@on

      –  NAT
traversal

•  Performance
sufficient
for
typical
home
deployments

      –  4
parallel
streams
with
30
percent
CPU
load
and
<
100
ms
delay



➙SVC
adapta@on
can
be
done
on
exis@ng
off‐the‐shelf

  network
devices

2009/07/01
            Chris@an
Timmerer,
Klagenfurt
University,
Austria
   14

References

•  I.
Kofler,
J.
Seidl,
C.
Timmerer,
H.
Hellwagner,
I.
Djama
and
T.
Ahmed,

   “Using
MPEG‐21
for
cross‐layer
mul@media
content
adapta@on”,
Journal

   on
Signal,
Image
and
Video
Processing,
Springer,
vol.
2,
no.
4,
Dec.
2008.

•  R.
Kuschnig,
I.
Kofler,
M.
Ransburg,
H.
Hellwagner,
“Design
op@ons
and

   comparison
of
in‐network
H.264/SVC
adapta@on”,
Journal
of
Visual

   Communica@on
and
Image
Representa@on,
Sept.
2008.

•  I.
Kofler,
M.
Prangl,
R.
Kuschnig,
and
H.
Hellwagner,
“An
H.264/SVC‐based

   adapta@on
proxy
on
a
WiFi
router”,
Proceedings
of
the
18th
Interna@onal

   Workshop
on
Network
and
Opera@ng
Systems
Support
for
Digital
Audio

   and
Video
(NOSSDAV
2008),
Braunschweig,
Germany,
May
2008.

•  I.
Kofler,
C.
Timmerer,
H.
Hellwagner,
and
T.
Ahmed:
“Towards
MPEG‐21‐
   based
Cross‐layer
Mul@media
Content
Adapta@on”,
Proc.
2nd

   Interna@onal
Workshop
on
Seman@c
Media
Adapta@on
and

   Personaliza@on
(SMAP
2007),
London,
UK,
Dec.
2007.





2009/07/01
             Chris@an
Timmerer,
Klagenfurt
University,
Austria
   15

Thank
you
for
your
a;en@on



              ...
ques@ons,
comments,
etc.
are
welcome
…





                                                            
Ass.‐Prof.
Dipl.‐Ing.
Dr.
Chris@an
Timmerer

                                   Klagenfurt
University,
Department
of
Informa@on
Technology
(ITEC)

                                                Universitätsstrasse
65‐67,
A‐9020
Klagenfurt,
AUSTRIA

                                                                  chris@an.@mmerer@itec.uni‐klu.ac.at

                                                                         h;p://research.@mmerer.com/

                                                     Tel:
+43/463/2700
3621
Fax:
+43/463/2700
3699

                                                                                  ©
Copyright:
Chris.an
Timmerer





2009/07/01
              Chris@an
Timmerer,
Klagenfurt
University,
Austria
                                         16


Weitere ähnliche Inhalte

Was ist angesagt?

CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR SystemsCAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR SystemsAlpen-Adria-Universität
 
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive StreamingMiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive StreamingAlpen-Adria-Universität
 
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...mgrafl
 
Bandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked StreamingBandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked StreamingAlpen-Adria-Universität
 
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...Alpen-Adria-Universität
 
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive StreamingState of the Art and Challenges AheadHTTP Adaptive StreamingState of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges AheadAlpen-Adria-Universität
 
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...Alpen-Adria-Universität
 
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingAlpen-Adria-Universität
 
Video Quality Measurements
Video Quality MeasurementsVideo Quality Measurements
Video Quality MeasurementsYoss Cohen
 
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video StreamingOn Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video StreamingAlpen-Adria-Universität
 
A Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
A Distributed Approach for Bitrate Selection in HTTP Adaptive StreamingA Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
A Distributed Approach for Bitrate Selection in HTTP Adaptive StreamingFörderverein Technische Fakultät
 
Dynamic Adaptive Streaming over HTTP (DASH)
Dynamic Adaptive Streaming over HTTP (DASH)Dynamic Adaptive Streaming over HTTP (DASH)
Dynamic Adaptive Streaming over HTTP (DASH)Alpen-Adria-Universität
 
HTTP Adaptive Streaming – Where Is It Heading?
HTTP Adaptive Streaming – Where Is It Heading?HTTP Adaptive Streaming – Where Is It Heading?
HTTP Adaptive Streaming – Where Is It Heading?Alpen-Adria-Universität
 

Was ist angesagt? (20)

CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR SystemsCAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR Systems
 
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive StreamingMiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
 
An Introduction to OMNeT++ 5.4
An Introduction to OMNeT++ 5.4An Introduction to OMNeT++ 5.4
An Introduction to OMNeT++ 5.4
 
20 Years of Streaming in 20 Minutes
20 Years of Streaming in 20 Minutes20 Years of Streaming in 20 Minutes
20 Years of Streaming in 20 Minutes
 
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
 
Bandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked StreamingBandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked Streaming
 
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
 
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive StreamingState of the Art and Challenges AheadHTTP Adaptive StreamingState of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges Ahead
 
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
 
Dynamic Adaptive Point Cloud Streaming
Dynamic Adaptive Point Cloud StreamingDynamic Adaptive Point Cloud Streaming
Dynamic Adaptive Point Cloud Streaming
 
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
 
HTTP Adaptive Streaming – Quo Vadis?
HTTP Adaptive Streaming – Quo Vadis?HTTP Adaptive Streaming – Quo Vadis?
HTTP Adaptive Streaming – Quo Vadis?
 
Video Quality Measurements
Video Quality MeasurementsVideo Quality Measurements
Video Quality Measurements
 
Adaptive Video over ICN @ IETF'87
Adaptive Video over ICN @ IETF'87Adaptive Video over ICN @ IETF'87
Adaptive Video over ICN @ IETF'87
 
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video StreamingOn Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
 
A Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
A Distributed Approach for Bitrate Selection in HTTP Adaptive StreamingA Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
A Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
 
Ctp
CtpCtp
Ctp
 
Dynamic Adaptive Streaming over HTTP (DASH)
Dynamic Adaptive Streaming over HTTP (DASH)Dynamic Adaptive Streaming over HTTP (DASH)
Dynamic Adaptive Streaming over HTTP (DASH)
 
Rtp
RtpRtp
Rtp
 
HTTP Adaptive Streaming – Where Is It Heading?
HTTP Adaptive Streaming – Where Is It Heading?HTTP Adaptive Streaming – Where Is It Heading?
HTTP Adaptive Streaming – Where Is It Heading?
 

Andere mochten auch

mHealth Summit EU 2015
mHealth Summit EU 2015mHealth Summit EU 2015
mHealth Summit EU 20153GDR
 
Center for connected medicine the surgeon can see you now
Center for connected medicine the surgeon can see you nowCenter for connected medicine the surgeon can see you now
Center for connected medicine the surgeon can see you now3GDR
 
Votado Como El Mejor Mail Del AñO (2)
Votado Como El Mejor Mail Del AñO (2)Votado Como El Mejor Mail Del AñO (2)
Votado Como El Mejor Mail Del AñO (2)Carlos Perez
 
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 20143GDR
 

Andere mochten auch (7)

mHealth Summit EU 2015
mHealth Summit EU 2015mHealth Summit EU 2015
mHealth Summit EU 2015
 
9
99
9
 
Diapositivas capitulo 4
Diapositivas capitulo 4Diapositivas capitulo 4
Diapositivas capitulo 4
 
Center for connected medicine the surgeon can see you now
Center for connected medicine the surgeon can see you nowCenter for connected medicine the surgeon can see you now
Center for connected medicine the surgeon can see you now
 
Votado Como El Mejor Mail Del AñO (2)
Votado Como El Mejor Mail Del AñO (2)Votado Como El Mejor Mail Del AñO (2)
Votado Como El Mejor Mail Del AñO (2)
 
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014
David Doherty presentation on mHealth Best Practice at Doctors 2.0 and You 2014
 
Pericia química legal
Pericia química legalPericia química legal
Pericia química legal
 

Ähnlich wie Media-Aware Network Elements on Legacy Devices

Optimizing User QoE through Overlay Routing, Bandwidth ...
Optimizing User QoE through Overlay Routing, Bandwidth ...Optimizing User QoE through Overlay Routing, Bandwidth ...
Optimizing User QoE through Overlay Routing, Bandwidth ...Videoguy
 
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...CA Technologies
 
06-dash.pptx
06-dash.pptx06-dash.pptx
06-dash.pptxAliIssa53
 
network basics
network basicsnetwork basics
network basicsAvin Ash
 
2002023
20020232002023
2002023pglehn
 
Delivering Traditional and Omnidirectional Media
Delivering Traditional and Omnidirectional MediaDelivering Traditional and Omnidirectional Media
Delivering Traditional and Omnidirectional MediaAlpen-Adria-Universität
 
Uit Presentation of IN/NGIN for Cosmote 2010
Uit Presentation of IN/NGIN for  Cosmote  2010Uit Presentation of IN/NGIN for  Cosmote  2010
Uit Presentation of IN/NGIN for Cosmote 2010michael_mountrakis
 
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERS
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERSROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERS
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERSDeepak Shankar
 
Globecom 2015: Adaptive Raptor Carousel for 802.11
Globecom 2015: Adaptive Raptor Carousel for 802.11Globecom 2015: Adaptive Raptor Carousel for 802.11
Globecom 2015: Adaptive Raptor Carousel for 802.11Andrew Nix
 
2018 FRSecure CISSP Mentor Program- Session 7
2018 FRSecure CISSP Mentor Program- Session 72018 FRSecure CISSP Mentor Program- Session 7
2018 FRSecure CISSP Mentor Program- Session 7FRSecure
 
Providing Controlled Quality Assurance in Video Streaming ...
Providing Controlled Quality Assurance in Video Streaming ...Providing Controlled Quality Assurance in Video Streaming ...
Providing Controlled Quality Assurance in Video Streaming ...Videoguy
 
CloudLightning and the OPM-based Use Case
CloudLightning and the OPM-based Use CaseCloudLightning and the OPM-based Use Case
CloudLightning and the OPM-based Use CaseCloudLightning
 
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMING
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMINGADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMING
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMINGVideoguy
 
WebRTC Real time media P2P, Server, Infrastructure, and Platform
WebRTC Real time media P2P, Server, Infrastructure, and PlatformWebRTC Real time media P2P, Server, Infrastructure, and Platform
WebRTC Real time media P2P, Server, Infrastructure, and PlatformRyan Jespersen
 
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP Networks
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP NetworksMulticasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP Networks
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP NetworksEditor IJMTER
 
A QoS-Adaptive Framework for Screen Sharing Over Internet
A QoS-Adaptive Framework for Screen Sharing Over InternetA QoS-Adaptive Framework for Screen Sharing Over Internet
A QoS-Adaptive Framework for Screen Sharing Over InternetDuc Nguyen
 
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeckDaniel Bimschas
 

Ähnlich wie Media-Aware Network Elements on Legacy Devices (20)

Optimizing User QoE through Overlay Routing, Bandwidth ...
Optimizing User QoE through Overlay Routing, Bandwidth ...Optimizing User QoE through Overlay Routing, Bandwidth ...
Optimizing User QoE through Overlay Routing, Bandwidth ...
 
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...
Pre-Con Education: Recognizing Your Network's Key Performance Indicators Th...
 
06-dash.pptx
06-dash.pptx06-dash.pptx
06-dash.pptx
 
network basics
network basicsnetwork basics
network basics
 
2002023
20020232002023
2002023
 
Delivering Traditional and Omnidirectional Media
Delivering Traditional and Omnidirectional MediaDelivering Traditional and Omnidirectional Media
Delivering Traditional and Omnidirectional Media
 
Uit Presentation of IN/NGIN for Cosmote 2010
Uit Presentation of IN/NGIN for  Cosmote  2010Uit Presentation of IN/NGIN for  Cosmote  2010
Uit Presentation of IN/NGIN for Cosmote 2010
 
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERS
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERSROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERS
ROLE OF DIGITAL SIMULATION IN CONFIGURING NETWORK PARAMETERS
 
Prashant Resume
Prashant ResumePrashant Resume
Prashant Resume
 
Globecom 2015: Adaptive Raptor Carousel for 802.11
Globecom 2015: Adaptive Raptor Carousel for 802.11Globecom 2015: Adaptive Raptor Carousel for 802.11
Globecom 2015: Adaptive Raptor Carousel for 802.11
 
2018 FRSecure CISSP Mentor Program- Session 7
2018 FRSecure CISSP Mentor Program- Session 72018 FRSecure CISSP Mentor Program- Session 7
2018 FRSecure CISSP Mentor Program- Session 7
 
Providing Controlled Quality Assurance in Video Streaming ...
Providing Controlled Quality Assurance in Video Streaming ...Providing Controlled Quality Assurance in Video Streaming ...
Providing Controlled Quality Assurance in Video Streaming ...
 
MPEG-DASH open source tools and cloud services
MPEG-DASH open source tools and cloud servicesMPEG-DASH open source tools and cloud services
MPEG-DASH open source tools and cloud services
 
CloudLightning and the OPM-based Use Case
CloudLightning and the OPM-based Use CaseCloudLightning and the OPM-based Use Case
CloudLightning and the OPM-based Use Case
 
[32]
[32][32]
[32]
 
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMING
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMINGADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMING
ADVANCES IN CHANNEL-ADAPTIVE VIDEO STREAMING
 
WebRTC Real time media P2P, Server, Infrastructure, and Platform
WebRTC Real time media P2P, Server, Infrastructure, and PlatformWebRTC Real time media P2P, Server, Infrastructure, and Platform
WebRTC Real time media P2P, Server, Infrastructure, and Platform
 
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP Networks
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP NetworksMulticasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP Networks
Multicasting Of Adaptively-Encoded MPEG4 Over Qos-Cognizant IP Networks
 
A QoS-Adaptive Framework for Screen Sharing Over Internet
A QoS-Adaptive Framework for Screen Sharing Over InternetA QoS-Adaptive Framework for Screen Sharing Over Internet
A QoS-Adaptive Framework for Screen Sharing Over Internet
 
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck
2013 09-02 senzations-bimschas-part1-smart-santander-facility-luebeck
 

Mehr von Alpen-Adria-Universität

VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesVEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesAlpen-Adria-Universität
 
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingGREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingAlpen-Adria-Universität
 
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Alpen-Adria-Universität
 
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionVEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionAlpen-Adria-Universität
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingAlpen-Adria-Universität
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Alpen-Adria-Universität
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...Alpen-Adria-Universität
 
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...Alpen-Adria-Universität
 
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Alpen-Adria-Universität
 
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Alpen-Adria-Universität
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamAlpen-Adria-Universität
 
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Alpen-Adria-Universität
 
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingMulti-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingAlpen-Adria-Universität
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentAlpen-Adria-Universität
 
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...Alpen-Adria-Universität
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesAlpen-Adria-Universität
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Alpen-Adria-Universität
 
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningVideo Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningAlpen-Adria-Universität
 
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...Alpen-Adria-Universität
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsAlpen-Adria-Universität
 

Mehr von Alpen-Adria-Universität (20)

VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesVEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
 
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video ProcessingGREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video Processing
 
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
 
VEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission PredictionVEEP: Video Encoding Energy and CO₂ Emission Prediction
VEEP: Video Encoding Energy and CO₂ Emission Prediction
 
Content-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive StreamingContent-adaptive Video Coding for HTTP Adaptive Streaming
Content-adaptive Video Coding for HTTP Adaptive Streaming
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
 
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...Empowerment of Atypical Viewers  via Low-Effort Personalized Modeling  of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
 
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...Optimizing Video Streaming  for Sustainability and Quality: The Role of Prese...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
 
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
 
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...Machine Learning Based Resource Utilization Prediction in the Computing Conti...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
 
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming StreamEvaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
 
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
 
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video StreamingMulti-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
 
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentPolicy-Driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
 
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
 
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesEnergy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
 
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
 
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningVideo Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
 
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...Optimizing  QoE and Latency of  Live Video Streaming Using  Edge Computing  a...
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
 
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsSARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
 

Kürzlich hochgeladen

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsMemoori
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 

Kürzlich hochgeladen (20)

Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial BuildingsAI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 

Media-Aware Network Elements on Legacy Devices

  • 1. Media‐Aware
Network
Elements
 on
Legacy
Devices

 m16695
 Ingo
Kofler,
Robert
Kuschnig,
and
Hermann
Hellwagner
 Chris:an
Timmerer
 Klagenfurt
University
(UNIKLU)

Faculty
of
Technical
Sciences
(TEWI)
 Department
of
Informa:on
Technology
(ITEC)

Mul:media
Communica:on
(MMC)
 h;p://research.@mmerer.com

h;p://blog.@mmerer.com

mailto:chris@an.@mmerer@itec.uni‐klu.ac.at
 Acknowledgement:
Part
of
this
work
is
supported
by
the
European
Commission
in
the
 context
of
the
and
ENTHRONE
(contract
no.
038463)
project.
Further
informa@on
is
 available
at
h;p://www.ist‐enthrone.org.


  • 2. Outline
 •  Mo@va@on
and
Introduc@on
 •  List
of
Technologies
 •  Architecture
and
Performance
Evalua@ons
 •  Demo
Video
 •  Conclusions
/
References
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 2

  • 3. Mo@va@on
and
Introduc@on
 •  Adapta@on
of
an
SVC
bitstream
 –  Achieved
by
removing
certain
NALUs
➙
filtering
of
NALUs
 –  Steered
by
a
(TID,
DID,
QID)
tuple
➙
filter
criteria
 –  Computa@onally
cheap
(compared
to
transcoding
etc.)
 •  Idea
 –  Perform
real‐@me
in‐network
adapta@on
of
the
SVC
bitstream
 on
an
ordinary,
low‐cost
WiFi
router
 –  Media‐aware
Network
Element
(MANE)
 •  Applica@ons
of
in‐network
adapta@on
 –  Cross‐layer
adapta@on
on
the
access
point
 –  Adapta@on
for
different
end‐devices
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 3

  • 4. Media‐aware
Network
Element
 On‐the‐fly
adapta@on
of
scalable
coded
video
content
in
a
media‐ aware
network
element
(MANE).
 Source:
h;p://ip.hhi.de/imagecom_G1/savce/
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 4

  • 5. List
of
Technologies
 •  Scalable
Video
Coding
(SVC)
 •  Real‐@me
Streaming
Protocol
(RTSP)
 –  Establishing
and
controlling
the
streaming
session
 –  VCR‐like
control
of
the
streaming
(Start,
Stop,
Pause)
 •  Real‐@me
Transport
Protocol
(RTP)
 –  Encapsulates
the
video
and/or
audio
content
 –  Mostly
used
on
top
of
the
unreliable
UDP
protocol
 –  Offers
sequence
number,
@mestamps
for
syncing
the
 playback
 –  Generic
header
with
content‐specific
payload
format
 (AVC,
SVC,
…)
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 5

  • 6. Proxy Approach server
 router
with
proxy
 router
 client
 RTSP
 RTP/RTCP
 RTSP
 RTP/RTCP
 RTSP
 RTP/RTCP
 TCP
 UDP
 TCP
 UDP
 TCP
 UDP
 IP
 IP
 IP
 MAC/PHY
 MAC/PHY
 MAC/PHY
 •  Proxy on network device between client & server –  Intercepts the RTSP / RTP communication –  Proxy is transparent for the client –  Acts as client for initial server and as server for the client •  Implications –  Proxy has to modify parts of the request (e.g. port numbers) –  Proxy can then adapt the SVC video stream carried over RTP 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 6

  • 7. Proxy Approach (cont’d)
 Benefits
 Drawbacks
 •  Session‐
and
stream‐/ •  Proxy
is
running
as
user‐ media‐awareness

 space
process

 •  Enables
stateful
inspec@on
 •  RTP
packets
have
to
be
 and
processing
of
packets
 passed
(copied)
from
the
 •  Allows
adapta@on
on
a
per‐ kernel‐space
to
the
user‐ session
basis
 space
and
vice
versa
 •  Consistent
RTCP
receiver
 •  Decreases
theore@cal
 and
sender
reports
 throughput
compared
to
 kernel‐internal
solu@on
(e.g.
 filtering
on
IP
level)
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 7

  • 8. Architecture
 Handles
incoming
RTSP
requests
(554
 redirect
to
proxy
using
iptables)
 SDP
stored
in
LRU‐based
cache
un@l
 session
established
 Maintains
state:
seq#,
@mestamps,
 SVC
params,
monitoring,
etc.
 Forwards
non‐SVC
packets
 Adapts
SVC
packets
 Acts
as
server
for
client
 Acts
as
client
for
server
 Retrieve
monitoring
informa@on
 Modify
adapta@on/SVC
parameters
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 8

  • 9. Performance
Evalua@ons
 •  Hardware:
Linksys
WRT
54
GL
 –  Broadcom
System‐on‐Chip
BCM5352EL
 –  MIPS32
200
MHz
CPU
 –  16
MB
RAM
 –  4
MB
Flash
Memory
 –  IEEE
802.11b/g
WLAN
 –  Fast
Ethernet
switch
with
5
ports
 –  price
~
45
Euros

(May
2008)
 •  Sonware:
OpenWrt
 –  Linux‐based
firmware
for
Broadcom‐based
WiFi
routers
 –  gcc-based SDK for OpenWrt available –  Proxy
implementa@on
in
ANSI
C
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 9

  • 10. Performance
Evalua@ons
(cont’d)
 •  Evalua@on
of
the
implementa@on
on
the
target
plaporm
 –  Server
–
proxy
–
client
deployment
 –  Inves@ga@on
of
worst‐case
scenario
(no
adapta@on)
 •  Performance
metrics
 –  CPU
usage
for
different
number
of
streams
 –  Delay
introduced
by
the
proxy
on
a
complete
access
unit
(frame)
 •  Three
different
SVC
streams
for
evalua@on
 –  Foreman,
CIF,
30
Hz,
715
kbps
 –  City,
4CIF,
30
Hz,
1247
kbps
 –  Harbour,
4CIF,
30
Hz,
2056
kbps
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 10

  • 11. Performance
Evalua@ons
(cont’d)
 CPU
Usage
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 11

  • 12. Performance
Evalua@ons
(cont’d)
 CDF of delay for sequence city (1245 kbps)
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 12

  • 13. Demo
Video
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 13

  • 14. Conclusions
 •  Proxy
approach
for
in‐network
adapta@on
on
a
per‐packet
 basis
 –  Aware
of
sessions
and
individual
streams
(media‐aware)
 –  Stateful,
not
a
simple
packet
dropper
 •  Applica@ons
 –  Adapta@on
according
to
device
capabili@es
 –  Cross‐layer
adapta@on
 –  NAT
traversal
 •  Performance
sufficient
for
typical
home
deployments
 –  4
parallel
streams
with
30
percent
CPU
load
and
<
100
ms
delay
 
➙SVC
adapta@on
can
be
done
on
exis@ng
off‐the‐shelf
 network
devices
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 14

  • 15. References
 •  I.
Kofler,
J.
Seidl,
C.
Timmerer,
H.
Hellwagner,
I.
Djama
and
T.
Ahmed,
 “Using
MPEG‐21
for
cross‐layer
mul@media
content
adapta@on”,
Journal
 on
Signal,
Image
and
Video
Processing,
Springer,
vol.
2,
no.
4,
Dec.
2008.
 •  R.
Kuschnig,
I.
Kofler,
M.
Ransburg,
H.
Hellwagner,
“Design
op@ons
and
 comparison
of
in‐network
H.264/SVC
adapta@on”,
Journal
of
Visual
 Communica@on
and
Image
Representa@on,
Sept.
2008.
 •  I.
Kofler,
M.
Prangl,
R.
Kuschnig,
and
H.
Hellwagner,
“An
H.264/SVC‐based
 adapta@on
proxy
on
a
WiFi
router”,
Proceedings
of
the
18th
Interna@onal
 Workshop
on
Network
and
Opera@ng
Systems
Support
for
Digital
Audio
 and
Video
(NOSSDAV
2008),
Braunschweig,
Germany,
May
2008.
 •  I.
Kofler,
C.
Timmerer,
H.
Hellwagner,
and
T.
Ahmed:
“Towards
MPEG‐21‐ based
Cross‐layer
Mul@media
Content
Adapta@on”,
Proc.
2nd
 Interna@onal
Workshop
on
Seman@c
Media
Adapta@on
and
 Personaliza@on
(SMAP
2007),
London,
UK,
Dec.
2007.
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 15

  • 16. Thank
you
for
your
a;en@on
 ...
ques@ons,
comments,
etc.
are
welcome
…
 
Ass.‐Prof.
Dipl.‐Ing.
Dr.
Chris@an
Timmerer
 Klagenfurt
University,
Department
of
Informa@on
Technology
(ITEC)
 Universitätsstrasse
65‐67,
A‐9020
Klagenfurt,
AUSTRIA
 chris@an.@mmerer@itec.uni‐klu.ac.at
 h;p://research.@mmerer.com/
 Tel:
+43/463/2700
3621
Fax:
+43/463/2700
3699
 ©
Copyright:
Chris.an
Timmerer
 2009/07/01
 Chris@an
Timmerer,
Klagenfurt
University,
Austria
 16