scispace - formally typeset

Proceedings ArticleDOI

A bio-inspired HTTP-based Adaptive Streaming player

11 Jul 2016-pp 1-4

TL;DR: A bio-inspired HAS optimisation design is piloted with the aim of maximising the overall user experience of a video playback session.

AbstractIn order to streamline video content distribution on a myriad of platforms over heterogeneous networks, HTTP Adaptive Streaming (HAS) is being increasingly adopted. In this paper we pilot a bio-inspired HAS optimisation design with the aim of maximising the overall user experience of a video playback session. Evaluations conducted within a real-world Internet environment, using quality indicators such as convergence time, start-up delay, average video rate, stability, and fairness, demonstrate the benefits of our design.

Summary (3 min read)

1. INTRODUCTION

  • HTTP-based adaptive video streaming service ensures that the quality of video each user receives is derived from context dependant information, e.g. throughput, buffer level, etc.
  • This requires a client to continuously monitor and estimate the resource availability.
  • It then requests a chunk with the highest video rate the estimated network and device capacity can sustain.
  • This unique mechanism allows HAS video services to adapt to different environments and changes in transmission channel states.
  • Inspired by the study of population dynamics in biology, this paper introduces a novel HAS player that maximises user experience.

2. BACKGROUND

  • HAS services provide audio-visual content in one or multiple adaptation sets of representations.
  • Each representation is ∗Thanks the Petroleum Technology Development Fund (PTDF), Nigeria for funding.
  • When there is a change of the estimated throughput, the media playback buffer level can be used as an indicator to determine whether to increase, decrease or keep the current quality level in order to maximise the playback quality whilst avoiding buffer under-run (i.e., re-buffering) [3, 4].
  • Based on this observation the authors were motivated to take a cross-disciplinary approach towards the QoE modelling of ABR algorithms.
  • This paper focuses on the most well-known population model, the Verhulst-Pearl logistic model.

3. VERHULST-PEARL BASED VIDEO RATE MAP

  • The authors assume that the rate at which the incoming video chunks arrives is analogous to birth rate and the rate at which a player consumes content from the play- back buffer is assumed to be the death rate.
  • Please note that the consumption rate is constant at one second of content every wall-clock second.
  • In their case, unlike in the wild, the maximum video rate is given (as defined in the MPD), therefore their task is focused on identifying the amount of the buffer required to guarantee the maximum video rate.
  • To derive the rate map the authors solve the differential equation (1) assuming that, like in a natural context, there is a seed population that reproduces to kick-start the growth.
  • The authors call this the minimum video quality level q0.

3.1. Implementation

  • A HAS player chooses video representations from a finite and discrete set.
  • The authors first restrict the player to switch between adjacent video rates only so as to prevent high amplitude variations.
  • Assuming qk has been completely downloaded (please recall that the authors are starting with (qmin), they can use equation (3) to calculate the buffer distance needed to change video rate.
  • If ∆Bk−1 = 0, the video rate is switched down, else the authors maintain the current video rate.

4. PERFORMANCE EVALUATION

  • The authors use the web services located at the Alpen-Adria-Universität, Klagenfurt, which hosts the Big Buck Bunny dataset [9].
  • The host that runs the player also hosts dummynet, tcpdump, lsof, and wget as part of the experiment.
  • The authors conducted a “blue-sky” test in both wired and wireless setting.
  • Then varies the link capacity using dummynet.
  • Each experiment was conducted 10 times and the average result is used.

4.1. Evaluation Metrics

  • There is a growing number of research in the field of QoE, and no definite model has so far been established for adaptive streaming [10, 11].
  • To evalaute the performance of their model, the authors employ the following metrics which are widely recognised as the key QoE indicators: Re-buffers: this is the total number of video freeze event per streaming session.
  • Two or more services are said to be fair if they divide the available resource among themselves equally, also known as Fairness.
  • Is the time taken to settle at the sustainable video rate, also known as Convergence time.

5.1. Startup Period and Stability

  • In the first set of the experimentation the authors stream in both the wired and wireless environments with the network link as is.
  • As can be see from Figure 2 the player is able to converge at the maximum video rate in a very short time and without a single instance of oscillation within the wired environment.
  • Furthermore, the start-up delay is minimal at 1.19s.
  • It’s worth noting that the player seems to be more cautious in a risky environment, delaying the video rate upgrade in order to maximises stability.
  • As can be seen from Figure 4(b) there are only three (3) instances where the player changes its video rate, with the oscillation factor of only 2.0%, even though the network is anything but stable as can be observed from Figure 4(a).

5.2. Responsiveness and utilisation

  • Next, the authors investigate how the propose player adapts in a network with severe bandwidth fluctuation.
  • Furthermore, when the link capacity suddenly decreases, the player does not instantly over-react like many other HAS player designs, rather because it senses the buffer can sustain high video rate it takes a modest approach, and when it starts reducing the video rate it does that linearly.
  • This confirms the fact that with careful design, a player can download above the link capacity, provided that some content is pre-buffered, without affecting its QoE performance.

5.3. Fairness

  • Also investigated is how fair the player is to other HAS player and background TCP traffic.
  • A total of four players, all using the same implementation, were run concurrently.
  • The authors set the maximum bandwidth to 6Mb/s, the hypothesis is that if the players are fair to one another they should equally share the available bandwidth (because all the players are connected to the same network and are also running on similar device).
  • This is achieved with a high level of stability, as none of the players observed more than three unnecessary oscillation of the video rate.
  • Next, the background traffic (file downloading from the same server) is started 30s after the start of the streaming session.

6. CONCLUSIONS

  • HTTP adaptive streaming provides a great foundation for online media streaming over heterogeneous networks.
  • Recent years have seen an increasing amount of efforts in the development of adaptation algorithms with the shared objectives of maximising the quality of user experiences.
  • This paper pilots a new approach that balances multiple key quality factors using a bio-inspired mechanism.
  • Evaluation results demonstrate the effectiveness of the proposed design with respect to start-up delay, convergence time, stability, network efficiency, average video quality and fairness.

Did you find this useful? Give us your feedback

...read more

Content maybe subject to copyright    Report

A BIO-INSPIRED HTTP-BASED ADAPTIVE STREAMING PLAYER
Yusuf Sani
, Andreas Mauthe, Christopher Edwards
School of Computing and Communications
InfoLab2l, Lancaster University
Lancaster LA1 4WA, UK
y.sani,a.mauthe,c.edwards @lancaster.ac.uk
Mu Mu
Faculty of Arts, Science and Technology
The University of Northampton
Northampton NN2 6JB, UK
mu.mu@northampton.ac.uk
ABSTRACT
In order to streamline video content distribution on a myr-
iad of platforms over heterogeneous networks, HTTP Adap-
tive Streaming (HAS) is being increasingly adopted. In this
paper we pilot a bio-inspired HAS optimisation design with
the aim of maximising the overall user experience of a video
playback session. Evaluations conducted within a real-world
Internet environment, using quality indicators such as conver-
gence time, start-up delay, average video rate, stability, and
fairness, demonstrate the benefits of our design.
Index Terms HTTP Adaptive Streaming, Adaptive Bi-
trate Selection, Buffer-based Player
1. INTRODUCTION
HTTP-based adaptive video streaming service ensures that
the quality of video each user receives is derived from con-
text dependant information, e.g. throughput, buffer level, etc.
This requires a client to continuously monitor and estimate
the resource availability. It then requests a chunk with the
highest video rate the estimated network and device capac-
ity can sustain. This unique mechanism allows HAS video
services to adapt to different environments and changes in
transmission channel states. The main challenge of design-
ing an adaptation scheme is to seek an optimal balance be-
tween a number of QoE factors such as video rate and sta-
bility or occurrence of re-buffering. Inspired by the study of
population dynamics in biology, this paper introduces a novel
HAS player that maximises user experience. Evaluations con-
ducted within a real-world Internet environment demonstrate
the benefits of our design with respect to fast convergence,
low start-up delay, maximum utilisation, high average video
rate, high stability, fairness to other players and video freeze
avoidance.
2. BACKGROUND
HAS services provide audio-visual content in one or multi-
ple adaptation sets of representations. Each representation is
Thanks the Petroleum Technology Development Fund (PTDF), Nigeria
for funding.
encoded using a specific encoding configuration (such as bi-
trate and native frame resolution). A representation of content
is stored in a series of chunks (segments). Using a manifest
file such as Media Presentation Description (MPD), a HAS
client progressively requests chunk of a certain bitrate, from
the selected representation, based on the level of available re-
sources measured at the client side. Such a streaming method
is also known as adaptive bitrate selection (ABR). The first
generation of ABRs select a chunk with the bitrate just below
the estimated throughput derived from a predefined number of
previously downloaded chunks [1, 2]. When there is a change
of the estimated throughput, the media playback buffer level
can be used as an indicator to determine whether to increase,
decrease or keep the current quality level in order to max-
imise the playback quality whilst avoiding buffer under-run
(i.e., re-buffering) [3, 4]. Many of the current HAS services
are found to suffer from unnecessary re-buffering [5], unde-
sirable fluctuations [6] and sub-optimal video quality [6]. In-
stead of solely relying on heuristic, it is shown in [7] that
incorporating a system model that captures the relationship
between buffer state changes and video rate can improve the
QoE performance of a HAS player. Based on this observa-
tion we were motivated to take a cross-disciplinary approach
towards the QoE modelling of ABR algorithms.
Population dynamics is a branch of mathematical ecology
that is mainly concerned with how the population of species
grows and shrinks as well as the processes that influence the
changes e.g. resource and competing specie [8]. This pa-
per focuses on the most well-known population model, the
Verhulst-Pearl logistic model. The model is very well under-
stood and is known to be stable and converges at the maxi-
mum carrying capacity. We then propose a buffer-based ABR
algorithm that uses the Verhulst-Pearl equation to drive its rate
evolution map.
3. VERHULST-PEARL BASED VIDEO RATE MAP
The video quality level is assumed to be a specie whose
growth we are interested in, and the playback buffer is con-
sidered to be its habitat. We assume that the rate at which the
incoming video chunks arrives is analogous to birth rate and
the rate at which a player consumes content from the play-

Fig. 1. Experimental Set-up
back buffer is assumed to be the death rate. It is known that
the rate of chunk arrival is
c(t
i
)
Q
[5], where c(t
i
) is the through-
put of a link and Q is bitrate of the chunk being downloaded.
Please note that the consumption rate is constant at one sec-
ond of content every wall-clock second. Therefore, buffer
level changes B
t
at any time t depends on these two param-
eter. AS in a natural habitat, the buffer size will determine
a limit on the maximum video rate q
max
a player can down-
load. In our case, unlike in the wild, the maximum video rate
is given (as defined in the MPD), therefore our task is focused
on identifying the amount of the buffer required to guarantee
the maximum video rate. We now modify the Verhulst-Pearl
equation thus as:
dQ
dB
= αq(q
max
q) (1)
Equation (1) defines the only valid system state-space,
which is represented by the rate at which video rate changes
with respect to buffer state. With α being the growth con-
stant and q the requested video rate. To derive the rate map
we solve the differential equation (1) assuming that, like in a
natural context, there is a seed population that reproduces to
kick-start the growth. We call this the minimum video quality
level q
0
.
Q =
q
max
1 + [
q
max
q
0
1]e
αq
max
B
t
(2)
It is easy to show that equation (2) will always converge
at the maximum video rate, that is, lim
B→∞
Q = q
max
[7].
3.1. Implementation
A HAS player chooses video representations from a finite and
discrete set. We first restrict the player to switch between ad-
jacent video rates only so as to prevent high amplitude varia-
tions.
Assuming q
k
has been completely downloaded (please re-
call that we are starting with (q
min
), we can use equation (3)
to calculate the buffer distance needed to change video rate.
Once B
k+1
= 0 we move up the ladder of video rates. If
B
k1
= 0, the video rate is switched down, else we main-
tain the current video rate.
B
k±1
=
Q
1
(q
k+1
) B
t
q
k+1
B
t
Q
1
(q
k1
) q
k1
(3)
4. PERFORMANCE EVALUATION
The experimental set-up is shown in Figure 1. The client is
connected to the Internet either via an Ethernet switch or EE’s
3G networks in the UK. We use the web services located at
the Alpen-Adria-Universit
¨
at, Klagenfurt, which hosts the Big
Buck Bunny dataset [9]. The player is implemented in Python
and uses Request package for HTTP request-response trans-
actions, and is tested on Ubuntu 12.04.2 LTS with 3.8 kernel.
The host that runs the player also hosts dummynet, tcpdump,
lsof, and wget as part of the experiment.
We conducted a “blue-sky” test in both wired and wireless
setting. Then varies the link capacity using dummynet. We
set B
max
= 200s, and the the growth constant α = 0.05
is used. Each experiment was conducted 10 times and the
average result is used. When more than one player is used
or when a player operates in the presence of a background
traffic, the experiment is conducted on the same machine for
the maximum portability.
4.1. Evaluation Metrics
There is a growing number of research in the field of QoE,
and no definite model has so far been established for adaptive
streaming [10, 11]. To evalaute the performance of our model,
we employ the following metrics which are widely recognised
as the key QoE indicators:
Re-buffers: this is the total number of video freeze
event per streaming session.
Average video rate: is calculated as
t
1
q
1
+t
2
q
2
...t
n
q
n
t
n
t
1
and
measured in kb/s [12].
Stability: measures the degree of video quality oscilla-
tion as described in [13].
Utilisation of available network resource: is calculated
by dividing the average video rate by the average net-
work capacity [14].
Fairness: two or more services are said to be fair if
they divide the available resource among themselves
equally.
Convergence time: is the time taken to settle at the sus-
tainable video rate.
Startup Delay: in this paper we define startup delay as
the amount of time it takes for a player to download 15
chunks before the playback starts.
5. RESULTS
This section discusses the result of the various test-bed exper-
iments conducted.
5.1. Startup Period and Stability
In the first set of the experimentation we stream in both the
wired and wireless environments with the network link as is.
As can be see from Figure 2 the player is able to converge
at the maximum video rate in a very short time and without
a single instance of oscillation within the wired environment.
Furthermore, the start-up delay is minimal at 1.19s. A similar
performance is obtained when the player is run in a wireless
environment (see Table 1 the summary). It’s worth noting
that the player seems to be more cautious in a risky environ-
ment, delaying the video rate upgrade in order to maximises

Table 1. Adaptation for Variable Bandwidth
Environment Startup
Delay
(s)
Convergence
Time (s)
Maximum
Rate
(kb/s)
Wired 1.19 16 8000
Wireless 1.25 91 8000
Fig. 2. Blue Sky Test (Wired Network)
stability. As can be seen from Figure 4(b) there are only three
(3) instances where the player changes its video rate, with the
oscillation factor of only 2.0%, even though the network is
anything but stable as can be observed from Figure 4(a).
5.2. Responsiveness and utilisation
Next, we investigate how the propose player adapts in a net-
work with severe bandwidth fluctuation. Figure 3 clearly
demonstrates that the player is able to converge at exactly
the system capacity regardless of whether it is an upward or
downward convergence. Furthermore, when the link capacity
suddenly decreases, the player does not instantly over-react
like many other HAS player designs, rather because it senses
the buffer can sustain high video rate it takes a modest ap-
proach, and when it starts reducing the video rate it does that
linearly. As is summarised in Table 2, the design maximises
the utilisation of link capacity at 106% without causing any
video freeze or resulting in buffer dropping to a risky level.
This confirms the fact that with careful design, a player can
download above the link capacity, provided that some content
is pre-buffered, without affecting its QoE performance.
5.3. Fairness
Also investigated is how fair the player is to other HAS player
and background TCP traffic. A total of four players, all using
the same implementation, were run concurrently. We set the
maximum bandwidth to 6Mb/s, the hypothesis is that if the
players are fair to one another they should equally share the
available bandwidth (because all the players are connected to
the same network and are also running on similar device). As
can be seen from Figure 5(a) none of the players secures more
than 1.5Mb/s, a fair share of the available capacity. This is
Fig. 3. Responsive and Utilisation test
Table 2. Adaptation for Variable Bandwidth
Rebuffers Maximum
Video rate
(kb/s)
Average
Video
rate(kb/s)
Throughput
Utilisation
(%)
0 6000 3827 106
achieved with a high level of stability, as none of the players
observed more than three unnecessary oscillation of the video
rate.
Next, the background traffic (file downloading from the
same server) is started 30s after the start of the streaming ses-
sion. As can be seen in Figure 5(b), as soon as the background
traffic is started the video quality starts to gradually drop un-
til an equilibrium is reached. They fairly share the available
bandwidth, that is, each uses about 3Mb/s. Furthermore, it
is worth noting that the drop in video rate does not affect the
stability of players, start-up delay or other QoE factors.
6. CONCLUSIONS
HTTP adaptive streaming provides a great foundation for on-
line media streaming over heterogeneous networks. Recent
years have seen an increasing amount of efforts in the devel-
opment of adaptation algorithms with the shared objectives
of maximising the quality of user experiences. This paper pi-
lots a new approach that balances multiple key quality factors
using a bio-inspired mechanism. Evaluation results demon-
strate the effectiveness of the proposed design with respect to
start-up delay, convergence time, stability, network efficiency,
average video quality and fairness.
7. REFERENCES
[1] Saamer Akhshabi, Ali C. Begen, and Constantine
Dovrolis, An experimental evaluation of rate-
adaptation algorithms in adaptive streaming over http,
in Proc. of the MMSys, 2011, pp. 157–168.
[2] Truong Cong Thang, Quang-Dung Ho, Jung Won Kang,
and Anh T Pham, Adaptive streaming of audiovisual
content using mpeg dash, Consumer Electronics, IEEE
Transactions on, vol. 58, no. 1, pp. 78–85, 2012.

(a) Blue Sky Test (wireless)
(b) Player Oscillation
Fig. 4. Four players streaming at the same time.
(a) Four Players streaming at the same time.
0
1000
2000
3000
4000
5000
6000
7000
8000
0 50 100 150 200 250 300 350
0
20
40
60
80
100
120
[Kb/s]
Buffer Level (s)
Elapse Time(s)
Bitrate
Throughput
Bandwidth
Buffer Occupancy
(b) Player and Background TCP traffic.
Fig. 5. Fairness test
[3] Saamer Akhshabi, Lakshmi Anantakrishnan, Ali C.
Begen, and Constantine Dovrolis, “What happens
when http adaptive streaming players compete for band-
width?, in Proc. of the NOSSDAV, 2012, pp. 9–14.
[4] Junchen Jiang, Vyas Sekar, and Hui Zhang, “Improving
fairness, efficiency, and stability in http-based adaptive
video streaming with festive, in Proc of the 8th Inter-
national Conference on Emerging Networking Experi-
ments and Technologies. ACM, 2012, pp. 97–108.
[5] Te-Yuan Huang, Ramesh Johari, and Nick McKeown,
“Downton abbey without the hiccups: Buffer-based rate
adaptation for http video streaming, in Proc. of the
FhMN, 2013, pp. 9–14.
[6] Te-Yuan Huang, Nikhil Handigol, Brandon Heller, Nick
McKeown, and Ramesh Johari, “Confused, timid, and
unstable: Picking a video streaming rate is hard, in
Proc of the ACM IMC, 2012, pp. 225–238.
[7] Yusuf Sani, Andreas Mauthe, and Christopher Edwards,
“Modelling video rate evolution in adaptive bitrate se-
lection, in The IEEE International Symposium on Mul-
timedia (ISM 2015), Dec 2015.
[8] John Pastor, Mathematical ecology of populations and
ecosystems, John Wiley & Sons, 2011.
[9] Stefan Lederer, Christopher Muller, and Christian Tim-
merer, “Dynamic adaptive streaming over http dataset,
in Proc. of the MMsys, 2012, pp. 89–94.
[10] Chao Chen, Lark Kwon Choi, G. de Veciana, C. Cara-
manis, R.W. Heath, and AC. Bovik, A dynamic sys-
tem model of time-varying subjective quality of video
streams over http, in Acoustics, Speech and Signal Pro-
cessing (ICASSP), 2013 IEEE International Conference
on, May 2013, pp. 3602–3606.
[11] Lark Kwon Choi, Yiting Liao, and Alan C Bovik,
“Video qoe models for the compute continuum, E-
LETTER, 2013.
[12] Florin Dobrian, Vyas Sekar, Asad Awan, Ion Stoica,
Dilip Joseph, Aditya Ganjam, Jibin Zhan, and Hui
Zhang, “Understanding the impact of video quality on
user engagement, ACM SIGCOMM Computer Com-
munication Review, vol. 41, no. 4, pp. 362–373, 2011.
[13] Christopher Mueller, Stefan Lederer, Reinhard Grandl,
and Christian Timmerer, “Oscillation compensating dy-
namic adaptive streaming over http, in Multimedia and
Expo (ICME), 2015 IEEE International Conference on.
IEEE, 2015, pp. 1–6.
[14] Guibin Tian and Yong Liu, “Towards agile and smooth
video adaptation in dynamic http streaming, in Proc. of
the CoNEXT, 2012, pp. 109–120.
Citations
More filters

Journal ArticleDOI
TL;DR: This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming, decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module.
Abstract: HTTP adaptive streaming (HAS) is the most recent attempt regarding video quality adaptation. It enables cheap and easy to implement streaming technology without the need for a dedicated infrastructure. By using a combination of TCP and HTTP it has the advantage of reusing all the existing technologies designed for ordinary web. Equally important is that HAS traffic passes through firewalls and works well when NAT is deployed. The rate adaptation controller of HAS, commonly called adaptive bitrate selection (ABR), is currently receiving a lot of attention from both industry and academia. However, most of the research efforts concentrate on a specific aspect or a particular methodology without considering the overall context. This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming. It starts by decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module. Each subcomponent encapsulates a particular function that is vital to the operation of an ABR scheme. A review of each of the subcomponents and how they interact with each other is presented. Furthermore, those external factors that are known to have a direct impact on the performance of an ABR module, such as content nature, CDN, and context, are discussed. In conclusion, this paper provides an extensive reference for further research in the field.

48 citations


Cites methods from "A bio-inspired HTTP-based Adaptive ..."

  • ...In [129], the relationship between buffer state change and video rate is modelled using a bio-inspired approach....

    [...]


Proceedings ArticleDOI
01 Oct 2017
TL;DR: A novel framework that considers the user QoE to adapt the video quality, named Optimized Quality of DASH (OQD) is proposed, to optimize users experience, and maximize the bandwidth usage.
Abstract: Video streaming has become a main contributor in an ever increasing Internet traffic, and meets the users expectation is a challenging task for both the Network service Provider (NsP) and Content service Provider (CsP). In this context, a new metric called: Quality of Experience (QoE) is evolved to measure the user satisfaction using video service, and it becomes a key driver for achieving the business goal of NsP and CsP. In this perspective, we have proposed a novel framework that considers the user QoE to adapt the video quality, named Optimized Quality of DASH (OQD). The objective of the proposed OQD framework is to optimize users experience, and maximize the bandwidth usage. A Machine Learning (ML) approach based on GRadient Boosting (GRB) method is implemented to predict the user QoE that considers three important network and application QoE Influence Factors (QoE IFs). We use the Reinforcement Learning (RL) approach to select the optimal video quality segment, which improves the user QoE. The performance of the proposed method is evaluated and compared against Greedy adaptive bit-rate method in terms of re-buffering, bandwidth utilization, average MOS, and standard deviation MOS. The results clearly show that proposed method performs well, as it considers the user’s perceived video quality as a regulator to optimize the overall video delivery network.

4 citations


Cites methods from "A bio-inspired HTTP-based Adaptive ..."

  • ...To evaluate the efficiency of our model, we use a set of metrics, which is explained in [13]....

    [...]


Journal ArticleDOI
TL;DR: An extensive evaluation of a QoE-aware video rate evolution model based on buffer state changes shows an improvement in the stability, average video rate and system utilisation, while at the same time a reduction in the start-up delay and convergence time is achieved by the modified players.
Abstract: HTTP adaptive video streaming matches video quality to the capacity of a changing context. A variety of schemes that rely on buffer state dynamics for video rate selection have been proposed. However, these schemes are predominantly based on heuristics, and appropriate models describing the relationship between video rate and buffer levels have not received sufficient attention. In this paper, we present a QoE-aware video rate evolution model based on buffer state changes. The scheme is evaluated within a real-world Internet environment. The results of an extensive evaluation show an improvement in the stability, average video rate and system utilisation, while at the same time a reduction in the start-up delay and convergence time is achieved by the modified players.

3 citations


Cites background from "A bio-inspired HTTP-based Adaptive ..."

  • ...While this work narrowed its investigation to a pattern, in our previous work [18], we presented a bioinspired model that pays attention to the whole sequence of the trajectory through the space of all possible system states....

    [...]


Proceedings ArticleDOI
01 Sep 2017
TL;DR: This paper investigates the performance of MPTCP when DASH clients use this protocol and shows that QoE achieved by the clients significantly changes due to congestion control algorithm thatMPTCP uses.
Abstract: Dynamic Adaptive HTTP Streaming (DASH) is a popular MPEG standard. In DASH systems, the clients adapt quality based on the observed network and internal parameters in order to achieve high Quality of Experience (QoE). The throughput of Internet applications can be increased if the underlying transport protocol utilizes the network conditions by using multiple end-to-end connections. MPTCP is a recent technology that allows clients to open multiple TCP connections without requiring a change in the client software. In this paper, we investigate the performance of MPTCP when DASH clients use this protocol. The experimental results show that QoE achieved by the clients significantly changes due to congestion control algorithm that MPTCP uses.

2 citations


Cites background from "A bio-inspired HTTP-based Adaptive ..."

  • ...Another factor that affects the quality selection is buffer level [2], [4]....

    [...]

  • ...Hence, various rate adaptation algorithms taking measured throughput and buffer level into account are proposed in the literature [2]–[4]....

    [...]


Proceedings ArticleDOI
Mu Mu1
19 Mar 2018
TL;DR: The software defined cognitive networking (SDCN) project aims at incorporating new developments in human cognition, media technology and communication networks to ensure the user experience, user-level fairness and network efficiency of online adaptive media.
Abstract: Adaptive media such as HTTP adaptive streaming (HAS) is becoming a standard tool for online video distribution. The non-cooperative competition of network resources between a growing number of adaptive streaming applications has a significant detrimental impact on the user experience and network efficiency. Existing network infrastructures often prioritise fast packet forwarding and not the quality of the delivered content. Future network management must leverage application and user-level cognitive factors to allocate scarce network resources effectively and intelligently. Our software defined cognitive networking (SDCN) project aims at incorporating new developments in human cognition, media technology and communication networks to ensure the user experience, user-level fairness and network efficiency of online adaptive media.

1 citations


Cites background from "A bio-inspired HTTP-based Adaptive ..."

  • ...Single-stream HAS optimisation algorithms [12], [8] are unaware of each other on the same network....

    [...]


References
More filters

Proceedings ArticleDOI
23 Feb 2011
TL;DR: This paper focuses on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluates two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF).
Abstract: Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.

713 citations


Proceedings ArticleDOI
10 Dec 2012
TL;DR: A principled understanding of bit-rate adaptation is presented and a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency are developed, which lead to a general framework for robust video adaptation.
Abstract: Many commercial video players rely on bitrate adaptation logic to adapt the bitrate in response to changing network conditions. Past measurement studies have identified issues with today's commercial players with respect to three key metrics---efficiency, fairness, and stability---when multiple bitrate-adaptive players share a bottleneck link. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited.In this paper, we present a principled understanding of bitrate adaptation and analyze several commercial players through the lens of an abstract player model. Through this framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bitrate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today's commercial players on all three key metrics across a range of experimental scenarios.

703 citations


Proceedings ArticleDOI
15 Aug 2011
TL;DR: This paper uses a unique dataset that spans different content types, including short video on demand, long VoD, and live content from popular video con- tent providers, to measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.
Abstract: As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.

633 citations


Proceedings ArticleDOI
22 Feb 2012
TL;DR: This paper presents their DASH dataset including the DASHEncoder, an open source DASH content generation tool, and provides basic evaluations of the different segment lengths, the influence of HTTP server settings, and shows some of the advantages as well as problems of shorter segment lengths.
Abstract: The delivery of audio-visual content over the Hypertext Transfer Protocol (HTTP) got lot of attention in recent years and with dynamic adaptive streaming over HTTP (DASH) a standard is now available. Many papers cover this topic and present their research results, but unfortunately all of them use their own private dataset which -- in most cases -- is not publicly available. Hence, it is difficult to compare, e.g., adaptation algorithms in an objective way due to the lack of a common dataset which shall be used as basis for such experiments. In this paper, we present our DASH dataset including our DASHEncoder, an open source DASH content generation tool. We also provide basic evaluations of the different segment lengths, the influence of HTTP server settings, and, in this context, we show some of the advantages as well as problems of shorter segment lengths.

418 citations


"A bio-inspired HTTP-based Adaptive ..." refers methods in this paper

  • ...We use the web services located at the Alpen-Adria-Universität, Klagenfurt, which hosts the Big Buck Bunny dataset [9]....

    [...]


Proceedings ArticleDOI
14 Nov 2012
TL;DR: This work measures three popular video streaming services -- Hulu, Netflix, and Vudu -- and finds that accurate client-side bandwidth estimation above the HTTP layer is hard, and rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video.
Abstract: Today's commercial video streaming services use dynamic rate selection to provide a high-quality user experience. Most services host content on standard HTTP servers in CDNs, so rate selection must occur at the client. We measure three popular video streaming services -- Hulu, Netflix, and Vudu -- and find that accurate client-side bandwidth estimation above the HTTP layer is hard. As a result, rate selection based on inaccurate estimates can trigger a feedback loop, leading to undesirably variable and low-quality video. We call this phenomenon the "downward spiral effect", and we measure it on all three services, present insights into its root causes, and validate initial solutions to prevent it.

347 citations


"A bio-inspired HTTP-based Adaptive ..." refers background in this paper

  • ...Many of the current HAS services are found to suffer from unnecessary re-buffering [5], undesirable fluctuations [6] and sub-optimal video quality [6]....

    [...]


Frequently Asked Questions (1)
Q1. What are the contributions mentioned in the paper "A bio-inspired http-based adaptive streaming player" ?

In this paper the authors pilot a bio-inspired HAS optimisation design with the aim of maximising the overall user experience of a video playback session.