Multiple Live Videos Delivery in Underprovisioned Networks

144 Pages·2017·7.01 MB·English

by  Jiayi Liu

Multiple Live Videos Delivery in Underprovisioned
Networks
Jiayi Liu
To cite this version:
Jiayi Liu. Multiple Live Videos Delivery in Underprovisioned Networks. Networking and Internet
Architecture [cs.NI]. Télécom Bretagne, Université de Rennes 1, 2013. English. ￿tel-00978741￿
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N° d’ordre : 2013telb0284

SSoouuss llee sscceeaauu ddee ll’’’’’’’’UUnniivveerrssiittéé eeuurrooppééeennnnee ddee BBrreettaaggnnee

Télécom Bretagne

En habilitation conjointe avec l’Université de Rennes 1

Ecole Doctorale – MATISSE

Multiple Live Videos Delivery in Underprovisioned Networks

Thèse de Doctorat

Mention : Informatique

Présentée par Jiayi Liu

Département : Informatique

Laboratoire : IRISA

Directeur de thèse : Antoine Beugnard

Soutenue le 4 Novembre 2013

Jury :

M. Laurent Schumacher, Professeur, University Namur (Rapporteur)
M. David Coudert, Professeur, INRIA (Rapporteur)
M. Antoine Beugnard, Professeur, Télécom Bretagne (Directeur de thèse)
M. Guillaume Pierre, Professeur, Université de Rennes I / IRISA (Président)
M. Cyrcil Concolato, Maître de conférences, Télécom ParisTech (Examinateur)
M. Juan Pedro Munoz Gea, Maître de conférences, Universidad Cartagena (Examinateur)
M. Gwendal Simon, Maître de conférences, Télécom Bretagne (Encadrant)

i
Acknowledgement
During this thesis, I have been able to work in both Brest and Rennes, where I met
many brilliant scientists, colleagues and hearty friends. The work presented in this
thesis received contributions from many of them in many ways. I appreciate this
chance to express my most sincere thanks to them, who helped me along the way.
First of all, I am heartily thankful to my supervisor Gwendal Simon. This thesis
would not have been possible without his help. Many thanks to his patient guidance,
encouragement, useful critiques and support from the initial to the (cid:28)nal level which
enabled me to develop an understanding of the subject. Also, I am very grateful for
his warm concern for my life in France, especially his enlightened policy on asking
for leaves, which enabled me to spend several Chinese New Year in China, as well as
stay bedside my mother throughout her illness.
I would like to express my sincere appreciation to Professor Antoine Beugnard,
the director of this thesis, for his advice and supervision. I would like to also thank
Annie Gravey for her constant support, and Jean-Marie Bonnin for taking me in the
RSM Department in Rennes.
I would like to also thanks the committee members for their e(cid:27)ort on making this
dissertation (cid:28)nished. Thanks them for accepting the invitation and spending their
precious time on improving this thesis.
My sincere appreciation to all my colleagues in our research group: Yaning Liu,
Jimmy Leblet, Fen Zhou, Eliya Buyukkaya, YiPing Chen, Zhe Li, Xu Zhang, Wei
You, and Karine Pires. I will never forget the pleasant days we spend together: the
insightful discussions and the agreeable co(cid:27)ee break chats. Moreover, thanks Qiao
Wang for her internship work.
I would like to show my gratitude to my coworkers. Their willingness to give their
time so generously has been very much appreciated. First of all, I would like to thank
the researchers from the CDN project: Raouf Hamzaoui and Shakeel Ahmad from
De Montfort University. My very great appreciation to Professor Raouf Hamzaoui,
for his valuable and constructive suggestions during the collaboration. Then, many
thanks to professor Catherine Rosenberg and Hanan Shpungin from University of
Waterloo, GØraldine Texier from TØlØcom Bretagne, the collaboration with them is
pleasant and fruitful.
My grateful thanks are also extended to all members of both Computer Science
DepartmentandNetworkSecurityandMultimediaDepartmentofTØlØcomBretagne.
Thanks Armelle Lannuzel and Anais Renaud for the organization of conference trips.
I am also very much indebted to all my friends in Brest and Rennes. Thanks their
warm companionship during these years.
Finally, I owe my deepest gratitude to my family. I am indebted to my boyfriend
Chen Wang. When I feel frustrated, his warm encouragement always buoyed me up.
Thanks to his company in both practical and spiritual life, the six years life in France
becomes more easier and full of good remembrance. I am indebted to my mother
Ailing Jia, a strong-minded, generous and a(cid:27)ectionate single-mother. It is not easy
for her to bring me up, and even support my study abroad. Although I was brought
up in a single-parent family, I lacked for nothing for she tried all her best in providing
me with good living conditions and giving me her entire love. She is the greatest
ii
mother in my heart.
iii
Abstract
With the proliferation of new categories of IP-enabled devices (such as smartphones,
tablets, etc.), nowadays, Internet users can ubiquitously access the online video ser-
vices. This promotes new types of services (for example, the user-generated live video
broadcasting), aswellasnewstreamingtechniques(suchasrate-adaptivestreaming).
Asaresult, scientistshaveobservedaformidablegrowthofInternettra(cid:30)cdominated
by the videos. A consequent challenge is the bandwidth availability problem(cid:22)a de-
livery network can be insu(cid:30)ciently provisioned under the heavy transmission burden
imposed by the huge volume of video tra(cid:30)c. Such underprovisioning problem is more
severe for live videos due to its real-time requirement.
In this thesis, we focus on bandwidth e(cid:30)cient video delivery solutions for live
streaming in underprovisioned video delivery networks. More speci(cid:28)cally, we target
to capture the aforementioned trends to (cid:28)nd solutions for: (1) user-generated live
streaming, and (2) rate-adaptive live streaming. We (cid:28)nally realized the following
contributions:
First of all, we built an multioverlay peer-to-peer (P2P) video sharing system
which allows ordinary Internet users to broadcast their own live videos. Typically,
suchasystemconsistsofmultipleindependentP2Plivevideostreamingsystems, and
facestheproblemof(cid:28)ndingasuitableallocationofpeeruploadbandwidth. Sofar,no
e(cid:30)cient solution has been proposed for the important case when the overall system
is underprovisioned, that is, when peers do not have enough upload bandwidth to
ensure a di(cid:27)usion of videos at full quality. We designed various objective functions
for this upload bandwidth allocation problem and showed how optimal solutions can
be e(cid:30)ciently computed. Simulation results demonstrated that our solutions improve
on existing algorithms in terms of video quality.
Then, we studied the problem of delivering live rate-adaptive streams in the con-
tent delivery network (CDN). For live streaming in underprovisioned CDN delivery
network, the goal is to maximize the throughput of the network. Previous theoretical
models that deal with streaming capacity problems do not capture the emerging real-
ityraisedbyrate-adaptivestreaming. Thus, weidenti(cid:28)edanewdiscretized streaming
model, which is more suitable for multiple live video channels in modern CDNs. For
thismodelweformulatedageneraloptimizationproblemthroughIntegerLinearPro-
gramming (ILP) and showed that it is NP-complete. Further, we presented a fast,
easy to implement, and near-optimal algorithm with approved approximation ratios
for a speci(cid:28)c scenario. This work is the (cid:28)rst step towards streaming multiple live
rate-adaptive videos in CDN and provides a fundamental theoretical basis for deeper
investigation.
Last, wefurtherextendedthediscretizedstreamingmodelintoanuser-centricone
which maximizes the overall satisfaction of an user population. The performance of
this user-centric discretized streaming model is approved through a set of toy-CDN
simulations. Further, we presented a practical system, which e(cid:30)ciently utilizes CDN
infrastructure to deliver live video streams to viewers in dynamic and large-scale
CDNs. The bene(cid:28)ts of our approaches on reducing the CDN infrastructure capacity
is validated through a set of realistic trace-driven large-scale simulations.
All in one, this thesis explores bandwidth e(cid:30)cient live video delivery solutions
iv
in underprovisioned delivery network for multiple streaming technologies. The aim
is to maximally utilize the bandwidth of relay nodes (peers in P2P and forwarding
equipments in CDN) to achieve an optimization goal.
Keywords: LiveStreaming,P2P,Multioverlay,CDN,rate-adaptivestreaming,DASH,
Underprovisioned System.
v
Contents
Acknowledgement i
Abstract iii
Contents vii
List of Figures x
List of Tables xi
RØsumØ xiii
1 Introduction 1
1.1 Status of Online Live Video Streaming . . . . . . . . . . . . . . . . . . 1
1.2 Motivations and Challenges . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 BandwidthconstraintforP2Puser-generatedlivevideosharing
system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Bandwidth constraint for live rate adaptive streaming over CDN 5
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3.1 A Multioverlay Peer-to-Peer Live Video Sharing System . . . . 6
1.3.2 Discretized streaming model for delivering live rate adaptive
videos over CDN . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.3 A user-centric live rate adaptive streaming system . . . . . . . 7
1.4 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Online Live Video Streaming 11
2.1 Live video streaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Peer-to-Peer Live Video Streaming . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Overall Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Tree-based P2P Systems . . . . . . . . . . . . . . . . . . . . . . 13
2.2.3 Mesh-based P2P Systems . . . . . . . . . . . . . . . . . . . . . 15
2.2.4 Multioverlay P2P Systems . . . . . . . . . . . . . . . . . . . . . 17
2.3 Live Video Streaming over CDN . . . . . . . . . . . . . . . . . . . . . 20
2.3.1 CDN: Overall Perspective . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 CDN Architecture for Live Streaming . . . . . . . . . . . . . . 21
2.3.3 Dynamic Adaptive Streaming over HTTP (DASH) . . . . . . . 23
vi CONTENTS
2.3.4 Live DASH over CDN . . . . . . . . . . . . . . . . . . . . . . . 25
3 Multioverlay P2P Video Sharing: Resource Allocation in Under-
Provisioned System 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1.1 Multioverlay P2P Live Video Sharing System . . . . . . . . . . 29
3.1.2 Our Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 Minimizing Under-provisioning: Bipartite Network Flow . . . . . . . . 33
3.4 Cost Function Driven Bandwidth Allocation Strategies . . . . . . . . . 35
3.4.1 Minimum-cost Maximum-(cid:29)ow Problem . . . . . . . . . . . . . . 35
3.4.2 Strategy I: Prioritize Overlay Diversity . . . . . . . . . . . . . . 35
3.4.3 Strategy II: Prioritize Overlay Popularity . . . . . . . . . . . . 36
3.4.4 Strategy III: Prioritize Fee-Paying Sources . . . . . . . . . . . . 37
3.4.5 Strategy IV: Prioritize User Preference . . . . . . . . . . . . . . 37
3.4.6 Practical Optimization . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Fair Bandwidth Allocation Strategy . . . . . . . . . . . . . . . . . . . 38
3.5.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5.2 Dual Decomposition Solution . . . . . . . . . . . . . . . . . . . 39
3.5.3 Distributed Algorithm . . . . . . . . . . . . . . . . . . . . . . . 40
3.6 Implementation and Practical Details . . . . . . . . . . . . . . . . . . . 41
3.6.1 Overall Architecture and Peer Dynamics . . . . . . . . . . . . . 41
3.6.2 Peer-Server Communication Overhead . . . . . . . . . . . . . . 42
3.6.3 Algorithm Computation Time . . . . . . . . . . . . . . . . . . . 42
3.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.7.1 Simulator Platform . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.7.2 Simulation Setting . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.7.3 Static Scenario Simulation . . . . . . . . . . . . . . . . . . . . . 47
3.7.4 Dynamic Scenario Simulation . . . . . . . . . . . . . . . . . . . 51
3.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4 Discretized Streaming Model for Live Rate Adaptive Streaming 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 System Model and Problem De(cid:28)nition . . . . . . . . . . . . . . . . . . 58
4.2.1 Live Rate Adaptive Streaming in CDN . . . . . . . . . . . . . . 58
4.2.2 Problem De(cid:28)nition . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Problem Formulation and Problem Complexity . . . . . . . . . . . . . 60
4.3.1 Integer Linear Programming formulation . . . . . . . . . . . . . 60
4.3.2 NP-completeness . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.4 A Practical Scenario and Algorithm . . . . . . . . . . . . . . . . . . . 64
4.4.1 Practical Bundle Delivery in CDN . . . . . . . . . . . . . . . . 64
4.4.2 The Bundle-Delivery Algorithm . . . . . . . . . . . . . . . . 64
4.4.3 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 68
4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . 71
CONTENTS vii
5 A User-centric Live Rate Adaptive Streaming CDN System 73
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.1.1 Our Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2 User-centric Discretized Streaming Model . . . . . . . . . . . . . . . . 75
5.2.1 User satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2.2 Live video streaming in a CDN . . . . . . . . . . . . . . . . . . 77
5.3 Formulation of the Capacity Problem . . . . . . . . . . . . . . . . . . . 78
5.3.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . . . . 78
5.3.2 Integer Linear Program Formulation . . . . . . . . . . . . . . . 79
5.4 Proof-of-Concept for User-centric Discretized Streaming . . . . . . . . 81
5.5 A Practical System: scadoosh . . . . . . . . . . . . . . . . . . . . . . 83
5.5.1 Type Speci(cid:28)ed User Assignment . . . . . . . . . . . . . . . . . 85
5.5.2 Utility-based Content Placement . . . . . . . . . . . . . . . . . 87
5.5.3 Utility driven delivery trees construction . . . . . . . . . . . . . 89
5.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.6.1 Simulation settings . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6 Conclusion 99
6.1 Synopsis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.2 Limitations and Perspectives . . . . . . . . . . . . . . . . . . . . . . . 101
Bibliography 114
Publications 115
Glossary 117