0 2 4 6 8 10 12 14 16 18 20 22 24
0 10 20 30 40 50 60
0 50 100 150 200 250 300
Percentage of overhead
Buffer level in seconds
5me (sec)
Buffer level ε
σ
overhead rule (ii)
rule (iii)
rule (ii) rule (i)
0 2 4 6 8 10 12 14 16 18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
Trhoughput (Mbps)
Segment Index
Server 7 Server 9 Server 8 Server 10 Server 6 Server 5 Server 3 Server 4 Server 2 Server 1 Server
bottleneck Client bottleneck
Unknown bottleneck Server
bottleneck
Percentage of Overhead (%)
Buffer Occupancy Level (sec)
ε σ
X p Joachim Bruneau-Queyreix
1,2, Mathias Lacaud
2, Daniel Negru
1, Jordi Mongay Batalla
3, Eugen Borcoci
41
Univ. Bordeaux - LaBRI,
2Viotech CommunicaLons,
3Warsaw Univ. of Technology,
4Univ. Politehnica of Bucharest
{jbruneau,negru}@labri.fr, {jbruneauqueyreix,mlacaud}@viotech.net, jordim@interfree.it, eugen.borcoci@elcom.pub.ro
- More users - More content - Higher demand for quality
=>
•Almost 82% of today’s internet traffic
• A t w o - f o l d increase by 2020
Traffic Forecasts
Context: Media Delivery in the Future Media Internet
The problem: QoE along with scalability costs are major issues in the design of future content delivery soluLons.
The challenges: Bandwidth aggregaLon on mulLple network paths; Adaptability to communicaLon channel heterogeneity; Uninterrupted video streaming; Quality- and server- adaptaLon.
ProposiLon: A DASH-evolving client-centric Mul3ple-Source Streaming (MS-Stream) protocol that simultaneously uses mulLple servers in order to provide high QoE and reliability in heterogeneous environments
Contribu7ons
MS-Stream: Dynamic Adap3ve Mul3ple-Source Streaming over HTTP
DASH: Dynamic Adap3ve Streaming over HTTP
Content Server Lme
Bi tr ate Ban dw id th
Lme
Lme
Bi tr ate
ObjecLves
1. The more the servers, the greater the QoE 2. Sub-segment loss or server failure =>
Playback conLnuity is insured
3. Any type of server parLcipates to the streaming up to its upload capacity
Content Delivery
(simultaneous use of servers)
Content Provider 500 Kbps
1 Mbps
Co nte nt In ge sL on
Content ReplicaLon
Resource Provider
(CDN, Cloud, Set-Top-Box, ISPs, IoT, etc.)
AdaptaLon to path
heterogeneity + sub-segment request
Overhead selecLon + Bitrate AdaptaLon Boileneck esLmaLon
+ Server AdaptaLon In-segment
download adaptaLon
Merge and display Inputs for segment n=n+1 (buffer level, servers’ throughput es7ma7on)
Server #1 Server #...
Server #M
(opLonal) List of servers to be used
Selected content bitrate
Prior-download adapta.on decisions for segment n Live adapta.on Manif
est file
A two-phase consump3on and adapta3on algorithm
Significant Results
GOP GOP
GOP GOP GOP GOP
Sub-segment 1 Sub-segment 2
GOP GOP
GOP GOP GOP GOP
GOP GOP #1
GOP GOP
GOP GOP
Sub-segment 3
High bitrate GoP
GOP
Redundant GoP at
criLcally low bitrate
GOP GOP
Null bitrate GoP
Sub-segment composer at
MS-Stream server Content quali7es available
at MS-Stream server
Time Time
Example of possible sub-segments
GOP #1 GOP GOP GOP
GOP GOP
GOP GOP GOP GOP
GOP GOP ...
1 Mbps
2 Mbps
1 Mbps
Up to a 4 Mbps
quality
Sub-segment request 1
Sub-segment delivery 3
Sub-segment composiLon 2
4 Sub-segment aggregaLon 5 AdaptaLon
QoE-aware Mul3ple-Server Streaming over HTTP
ICME 2017
References
The media delivery chain
Content
Owner Content
Provider
Technical CDN/
Enabler
Internet
Provider End-Users
$ DistribuLon contract $ Delivery contract $ Transit $ Internet subscripLons
$ OTT video streaming service subscripLon
- Highly efficient and scalable
- High operaLng and scaling costs
- Open over-provisioned infrastructure to meet end-users’ QoE demand
Content Delivery Networks
Pros:
- Avoid video freezing by adjusLng desired quality to the network condiLons
- Increase Quality of Experience (QoE)
Today’s and tomorrow’s challenges
Up to a 5 Mbps quality
Standard-compliant sub-segment composi3on A DASH-based streaming protocol architecture
HT TP C lie nt Sub-stream
Aggregator Standard Decoder AdaptaLon
MS-Stream Client
MS -S tr eam HT TP A PI DASH Storage
Sub-segment composer
3 Sub-segment delivery
4
2 5
MS-Stream Server
Sub-segment request 1
0 1 2 3 4 5 6 7 8 9 10
0 1 2 3 4 5 6 7 8 9 10
0 5 10 15 20 25 30 35 40 45 50 55
Server ID
Number of servers
Segment Index
Number of server server ID
Cancelled server Re-used server 0
2 4 6 8 10 12 14 16 18
0 5 10 15 20 25 30 35 40 45 50 55
Mbps
Segment Index
aggregated thoughput video bitrate
redundant data
✔ Codec-compliant
✔ Low complexity
✔ Redundancy tunability
✔ High possible number of sub- segment
Boileneck esLmaLon and server adaptaLon:
• EsLmaLng the presence and the type of boileneck (at server or at client side) based on TCP connecLons’ throughput stability in Lme (78% reliable);
• AddiLve-Increase MulLplicaLve-Decrease approach to adjust the number of simultaneously used servers (based on boilenecks, target bitrate).
Overhead selecLon:
• LimiLng and minimizing bandwidth consumpLon overhead due to GoP redundancy.
• MinimizaLon method based on client buffer occupancy
AdaptaLon to path heterogeneity:
• Assigning sub-segment delivery to servers according to their observed goodput.
In-segment download adaptaLon
• Monitor sub-segment downloads progress;
• Three in-segment download adaptaLon rules to cancel late sub-segment delivery or re-assign the delivery of missing GoPs to other servers.
Func3onal valida3on
A 10-server test-bed; a 10-min video; 8 bitrates (from 1 to 8 Mbps), redundant bitrate at 150Kbps
Evalua3on over the Internet ( DASH versus MS-Stream)
• J. Bruneau-Queyreix, M. Lacaud, D. Negru, J. Batalla, and E. Borcoci, « QoE Enhancement Through Cost-EffecLve AdaptaLon Decision Process for MulLple-Server Streaming over
HTTP » in IEEE Interna7onal Conference on Mul7media and Expo (ICME), 2017.
• J. Bruneau-Queyreix, M. Lacaud, D. Negru, J. Batalla, and E. Borcoci, « Adding a New Dimension to HTTP AdapLve Streaming through MulLple-Source CapabiliLes, » in IEEE Mul7Media Magazine (in press), 2017.
• J. Bruneau-Queyreix, M. Lacaud, D. Negru, J. Batalla, and E. Borcoci, « MS-Stream: A MulLple-Source AdapLve Streaming SoluLon Enhancing Consumers Perceived Quality, » in IEEE Consumer Communica7ons and Networking Conference (CCNC), 2017.
• J. Bruneau-Queyreix, M. Lacaud, D. Negru « A MulLple-Source AdapLve Streaming SoluLon Enhancing Consumers Perceived Quality, » in IEEE Consumer Communica7ons and Networking Conference (CCNC) demo track, 2017.
• J. Bruneau-Queyreix, M. Lacaud, D. Negru, J. Batalla, and E. Borcoci, « MulLple-DescripLon DASH: PragmaLc video streaming maximizing end-users’ quality of experience » in IEEE Interna7onal Conference on Communica7ons (ICC), 2016.
