QoE-enabled big video streaming for large-scale heterogeneous clients and networks in smart cities

SPECIAL SECTION ON SMART CITIES
Received November 21, 2015, accepted November 28, 2015, date of publication December 8, 2015,
date of current version February 29, 2016.
Digital Object Identifier 10.1109/ACCESS.2015.2506648
QoE-Enabled Big Video Streaming for Large-Scale
Heterogeneous Clients and Networks
in Smart Cities
BO-WEI CHEN1, (Member, IEEE), WEN JI2, (Member, IEEE),
FENG JIANG3, AND SEUNGMIN RHO4
1Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
2Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100864, China
3School of Computer Science, Harbin Institute of Technology, Harbin 150001, China
4Department of Multimedia, Sungkyul University, Anyang 430-742, Korea
Corresponding author: S. Rho (smrho@sungkyul.ac.kr)
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the
Ministry of Education (2013R1A1A2061978).
ABSTRACT The rapid growth of the next-generation communication and networks is bringing video
services into more pervasive environments. More and more users access and interact with video content
using different devices, such as smart televisions, personal computers, tablets, smartphones, and wearable
equipments. Providing heterogeneous Quality of Experience (QoE) that supports a wide variety of multime-
dia devices is critical to video broadcasting over the next-generation wireless network. This paper reviews
practical video broadcasting technologies and examines current requirements ranging from heterogeneous
devices to transmission technologies. Meanwhile, various coding methodologies, including QoE modeling,
scalable compression efficiency, and flexible transmission, are also discussed. Moreover, this paper presents a
typical paradigm as an example for video broadcasting with large-scale heterogeneity support, which enables
QoE mapping, joint coding, flexible forward error coding, and cross-layer transmission, as well as optimal
and dynamic adaptation to improve the overall receiving quality of heterogeneous devices. Finally, a brief
summary of the key ideas and a discussion of interesting open areas are summarized at the end of this paper
along with a future recommendation.
INDEX TERMS Quality of service, video coding, broadcast technology, communications technology.
I. INTRODUCTION
With the integration of telecommunications, television net-
works, and internet, future networks gradually become an
integrated medium with high broadbands and large-scale
traffic. Providing users with satisfactory Quality of Expe-
rience (QoE) that supports the mass heterogeneous multi-
media devices is essential for video broadcasting over the
next-generation networks. High-quality video services at any
devices, anytime and anywhere are becoming an inevitable
tendency. This significantly challenges the innovation and
development in both networks and video technologies.
Recent years have seen a flourishing change in video traf-
fic. Internet video traffic has already exceeded more than half
of any other traffic of consumer networks in 2013. Mobile
video streaming will grow at a compound annual growth rate
of 69.0% between 2013 and 2018, the highest growth rate of
any mobile applications, as reported in [1]. According to the
statistical report from the China Internet Network Informa-
tion Center (CNNIC) [2], the number of mobile-phone users
has reached 500 million with an annual growth rate of 19.1%.
The most noticeable growth also occurs in mobile-phone
users. The proportion of consumers that access the internet
through mobile phones increases from 74.5% in 2012 to
81.0% in 2013, much higher than that through the other
mobile devices.
Currently, with more global 4G deployments, higher band-
width, and more intelligent services, mobile applications pro-
viding various videos attract more users. For example, the
number of mobile video users in China exceeds 247 million
and increases to 83.8% in 2013 compared with that in 2012.
Mobile video streaming has become the major and primary
contributor to the growth of global mobile applications. Such
a trend significantly challenges wireless and internet service
providers as well as content providers. Therefore, how to
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B.-W. Chen et al.: QoE-Enabled Big Video Streaming for Large-Scale Heterogeneous Clients and Networks
FIGURE 1. Architecture of classical video broadcasting to heterogeneous devices.
ensure the high quality of user experience is of prior concerns.
Despite decades of research on video coding, communica-
tions, and networking, there still remain challenging tasks
to provide the mass interactive applications and satisfactory
QoE for mobile users with diverse devices.
Providing good QoE for the users with heterogeneous
multimedia devices is essential for video broadcasting in
the next-generation networks. Much effort has been made
to enhance transmission capacity to satisfy the exponential
increase of traffic-driven and highly-diverse devices. One
critical requirement for future ubiquitous environments is
the ability to handle the heterogeneity, such as various user
preferences, display characteristics, device capabilities, and
emerging interactive modes.
Therefore, researchers are intensively studying what the
core heterogeneous factor in video broadcasting is and how
new techniques should be designed for better QoE. This
article makes an investigation into the following questions.
• What heterogeneous factors need to be considered in
video broadcasting systems?
• How does a new intelligent interaction affect broadcast-
ing architectures?
• How do the researchers model QoE metrics to meet the
requirements of mass users?
• How do the service providers offer heterogeneous QoE
videos from the source side?
• How do the service providers efficiently broadcast video
streams in consideration for heterogeneous QoE that
supports heterogeneous circumstances?
• What are the future challenges?
To this end, this article surveys the aforementioned
possible challenges for heterogeneous video broadcasting
under heterogeneous circumstances. Feasible solutions to
the above questions are offered in this study by proposing
heterogeneous QoE coding and transmission schemes
from the perspectives of architectures, strategies, and
methodologies.
The remainder of this article is organized as follows.
Section II firstly gives the overview of the whole system
architecture. Subsequently, Sections III–VI present the above
challenges, followed by the foundation and a series of techni-
cally impressive solutions. Then, numerical results and con-
clusions are provided in Section VII. Conclusions are finally
drawn in Sections VIII and IX, along with recommendations
for future research.
II. OVERVIEW
In current video broadcasting systems, heterogeneity exists
almost everywhere, for example, user terminals, user experi-
ence, network access, network types, video contents, and data
support. Fig. 1 illustrates the key components for classical
video broadcasting. Different aspects, including devices,
networks, servers, video content, and clients, are discussed as
follows. On the device side, each video stream is broadcast
to various devices based on different characteristics rang-
ing from terminal display sizes, bandwidth requirements,
reception capabilities, channel conditions, battery capacities,
to energy consumptions. Moreover, devices on the network
side might access the same video service from different net-
works, such as DVB, WIFI, GSM (i.e., 2G systems) [3], [4],
WCDMA/CDMA2000/TD-SCDMA (i.e., 3G systems),
LTE/WIMAX (i.e., 4G systems) [5], or future 5G net-
works [6]. Regarding the server side, videos might be deliv-
ered via heterogeneous clouds and storages. Even when it
comes to video data, heterogeneity still exists in video con-
tent. For example, video data in different scalable domains
usually have different rate distortions. The last is the end-
user side, or the client side, where users might have various
98 VOLUME 4, 2016

preferences and interactions, so that they desire different
QoE even for the same video service.
Conventionally, transmissions, coding, and QoE measure-
ments are seldom designed in consideration of heteroge-
neous characteristics, and most of the above challenges are
practically ignored during video broadcasting. To provide
flexible and reliable QoE for broadcasting, it is necessary
to deeply exploit the heterogeneities in joint coding, trans-
missions, QoE measurements, and adaptivity. The feasible
solution for heterogeneous QoE provision in broadcasting
networks requires an integrated design effort. The next
subsection firstly gives a survey on the above key components
of video broadcasting systems, including QoE metrics, video
coding, and transmissions. Subsequently, a system with het-
erogeneous support is presented with the evaluation of system
performance in terms of broadcasting efficiency.
III. HETEROGENEITY IN QoE METRICS
The International Telecommunication Union (ITU) leads the
study group on QoS/QoE for IPTV services. According to
the definition by the ITU [7], the QoE is ‘‘The overall accept-
ability of an application or service, as perceived subjectively
by the end-user.’’ The QoE includes two major aspects –
Quality of Service (QoS) and human components. The for-
mer involves services, transmissions, and applications, which
are measured by objective methods. The latter depend on
emotions, preferences, experience, and so on. Unlike QoS,
human components are measured in subjective ways [8].
More details were discussed in [9], where the authors summa-
rized the QoE assessment and the corresponding standardiza-
tion activities of the ITU in detail. At present, QoE has been
gradually developed and has a wide range of applications in
academia and industry. QoE has outformed IPTV initially
desinged by the ITU.
So far, current multimedia services in communication
and network systems have achieved a certain level because
user-centric concepts have deeply affected the designs for
the whole process of network deployments, service acti-
vation, consumptions, management, and evolution several
years before. As the main metric of user-centric analysis,
QoE however has a complex layer-based archi-
tecture [11], [85]. In the Universal Mobile Telecommunica-
tion System (UMTS) [12], many techniques in the physical
layer already supported the concept of QoE, such as down-
link/uplink control and relevant radio/power resource man-
agements. However, in the next-generation network, the focus
lies in how to guarantee QoE across layers and to ensure QoE
between applications and transmission layers [13]. Since both
the UMTS and the next-generation systems were originally
built on the basis of QoS, the monitoring framework between
QoS and QoE becomes a feasible route.
The application layer concentrates on context-aware
end-to-end transmissions through quality control parame-
terizations [14]. Since the uniqueness of QoE lies in the
subjective perception of users and the ability of a system
to fulfill users’ expectations, QoE in the application layer
introduces more human components than current architec-
tures [14]. Unfortunately, it is still difficult to simultaneously
obtain the complete information of QoE parameters from all
users. Accordingly, Zhou et al. [15] proposed using dynamic
resource allocation to optimize the total subjective quality
of all the users with or without prior QoE information and
tests. As current applications, communications, and network
systems are gradually migrating to cloud-based architectures,
an universal model is therefore required to manage cloud-
computing ecosystems. Fortunately, QoE-based management
provides an interactive, rapid, and convenient solution to
cloud computing [10]. Through understanding, monitoring,
and estimating QoE, cloud-service providers can easily adapt,
control, and manage all the interactions between users and
servers. For instance, Laghari et al. [16] examined recent
QoE models and developed a practical taxonomy of the
relevant variables and interactions based on a communica-
tion ecosystem. Fig. 2 summarizes the major factors in a
QoE model. As illustrated in this figure, human-related
components are the key difference between the future and
conventional QoS architectures. As a user can access any
type of services through any device, like computers, laptops,
smartphones, tablets, wearable gadgets, and televisions,
personal experience is actually based on the features of termi-
nal equipments, including types, displays, batteries, and oper-
ational modes. How to formalize the actual preferences of a
user involves further research. This is because the personal
perceptual quality significantly depends on – Personal infor-
mation (e.g., ages, genders, professionals, and educations),
visual quality, video formats (e.g., High Definitions (HDs)
and Standard Definitions (SDs)), interactive modes
(e.g., videos on demand), urgent degree (e.g., on-display
proportions in an online video), content (e.g., news or
movies), costs of services, the presence of advertisements,
and environments (e.g., in a bus or at an office). In addition,
factors involving user subjective perceptions, like emotions
and personal habits, dominate satisfaction of a service.
Although, through experience parameter estimation and
extraction under a specific scenario, QoE control and assur-
ance can be rapidly deployed. Nevertheless, there is still no
consensus about an effective QoE model.
For multiuser video communication systems (e.g., broad-
casting), research on the relation between heterogeneous user
devices and video streams, which supports user-centric QoE,
has recently become a research hotspot [17].
Video sources can be roughly classified into the following
four categories – 1) Spatial domain that dominates display
sizes. 2) Temporal domain, which determines the fluency
of playback. 3) Quality domain that influences visual quality.
4) Error domain, which decides the reliability during broad-
casts. Since classical theories on image fidelity could not
simultaneously measure the above-mentioned four domains,
hence, current research [19]–[23] developed a hybrid-domain
method across controls by considering multidimensional
features of videos. For instance, Ou et al. [19] mod-
eled the impact of temporal and quality domains based
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FIGURE 2. QoE architecture in video communication systems with heterogeneous support.
on frame rates and quantization step-sizes by fitting frame
rates, fidelity of Signal-to-Noise Ratios (SNRs), and Mean-
Opinion Scores (MOSs) into a function. A subjective quality
test was conducted for evaluating the relation between the rate
and the perceptual quality of a scalable video with temporal
and quality scalability. Chan et al. [18] built multidistortional
measures between the fidelity of Peak SNRs (PSNRs) and
the temporal downsampling, as well as between the PSNR
fidelity and the spatial downsampling. They subsequently
applied the rate-distortion optimized scheduling to analyze
a diverse range of target devices. These methods [18], [19]
basically followed the idea of PSNR fidelity and were further
modified to support the subjective cross-measure with frame-
rate and resolution scalability.
Recently, more and more approaches addressed the
spatio-temporal quality problem by using user experience
maximization. Such studies include [19]–[22]. In [20],
Wang et al. created a generalized and classifier-based pre-
diction framework to provide multidimensional adaptive
operations and different SNR-temporal resolutions by using
the human vision system. Similarly, the authors [21], [22]
modeled the spatio-temporal utility through homogeneous
and heterogeneous QoE decomposition. Rather than focus-
ing on individual domains, the hybrid multiple distortion
measure [19] has become a tendency due to the effective
multidimensional feature of QoE.
IV. SCALABLE SUPPORT FOR VIDEO CODING
Broadcasting videos to multiple heterogeneous devices usu-
ally involves two major techniques – Coding and transmis-
sions. Thus, scalable control and its performance are critical
for broadcasting. From the view of video coding, today’s
video coding paradigm typically uses spatial and temporal
features as well as quality redundancies when serving a
diverse range of display resolutions and transmission chan-
nels. Cumulative video coding and non-accumulative coding
are typical examples.
The former, cumulative coding, classifies video sources
into one base layer and multiple enhancement layers. The
base layer can be independently decoded, whereas the
enhancement layer can be successfully decoded only when
the base layer and the anterior enhancement layers are recov-
ered. One of the most famous cumulative coding methods is
scalable video coding (SVC) [23], which has pioneered the
research trend in academia and industry for years. Since SVC
can dependently encode video sources based on video subsets
from generation sides to receiver sides, scalability can be
directly achieved based on different requirements, like quality
100 VOLUME 4, 2016

(fidelity), display sizes (spatial domain), and frame rates
(temporal domain). Multiresolutional source coding [24] pre-
sented another cumulative coding method, where embedded
data descriptions were given during encoding in the man-
ner of progressive and successive refinement. By doing so,
SVC has brought the video processing area into a mile-
stone and subsequently enabled encodes/decoders to process
a variety of rates and resolutions. Likewise, wavelet video
coding [25] was a particularly useful technique for spatio-
temporal scalability with low complexity. It has become
a popular algorithm in modern multiresolutional video
compression.
Unlike multiresolutional or layered source coding, as
mentioned in accumulative coding, there is no hierarchical
description in non-accumulative coding. Multiple description
coding (MDC) [26] was a typical instance and can be used for
the heterogeneity issue. This is because MDC can decom-
pose a video source into multiple descriptions and subse-
quently convert this video into several subsets based on the
descriptions.
Since MDC is performed in the encoded streams, user
devices benefit from path diversity over different net-
works when multiple paths are available [27]. For exam-
ple, if descriptions are not successfully received due
to unknown errors, packet loss, transmission delay, or
jitter, the decoder can still reconstruct the original video
from the received descriptions. Such a property provides
flexible robustness for multiple heterogeneous communica-
tion systems against noise [28]. Similar studies like [29]
devised a system that could assign a multiple description
video by constructing multiple multicast trees. Despite the
robustness of heterogeneous communications, MDC is still
susceptible to the problem of coding efficiency, for MDC
recovers a certain video quality from every description. How-
ever, since the probability of losing every description at
the same time is quite low, MDC still demonstrates satis-
factory reliability and robustness in practical transmission
systems.
In summary, based on the aforementioned reasons, video
coding techniques with scalable support have become widely
used in modern video broadcasting systems, such as mobile
broadcasting/multicasting [30], multiantennal broadcasting
systems [31], opportunistic broadcasting/multicasting [32],
and multimedia broadcasting networks [33].
V. VIDEO TRANSMISSION IN HETEROGENEOUS
CIRCUMSTANCES
With the time-varying and error-prone characteristics of
channels, the variety of devices, and the complex of QoE,
conventional video broadcasting usually faces unreliable
problems. To overcome such an issue, researchers have
developed a new field called reliable video broadcasting,
where reliability in wireless networks was realized via trans-
mission techniques. Most of related works focused on:
1) using opportunistic transmissions to improve the diversity
gain in multiuser scenarios; 2) developing cross-layer-based
forward error correction (FEC) to simultaneously provide
heterogeneous QoE support; 3) introducing fair streaming
schemes to satisfy variable requirements for multiple hetero-
geneous users. The following content respectively elaborates
these three categories.
1) Among the aforementioned three approaches, oppor-
tunistic transmissions exploited the variations in channels to
achieve high utilization of scarce wireless resources. Such
transmissions have revealed potentials in cross-layer and real-
time applications for wireless broadcasting networks. Related
works can be found in [34]–[37]. The authors [34] proposed
opportunistic spectrum selection that could allocate avail-
able channel resources orderly to users based on their QoE
expectations, with joint support of channel characteristics,
QoE measures, and current channel resources. In contrast,
another approach ‘‘opportunistic user selection’’ chose users
with maximum channel gains or states [35], [36] to improve
broadcasting efficiency. Opportunistic listening and condi-
tional demodulation among video layers [37] could enhance
the system performance. The work by Huang et al. [38]
showed that opportunistic-based layered multicasting could
obtain improvement in efficiency through suitable schedul-
ing and resource allocation. Consequently, the utilization
of limited resources was accordingly improved by oppor-
tunistically transmitting video substreams in considera-
tion of those heterogeneous characteristics and multiuser
requirements.
2) To guarantee the acceptable visual experience, QoE can
cowork with FEC and error protection strategy in a cross-
layer designed framework. Based on such an idea, the sec-
ond transmission category emphasizes joint channel coding,
resource allocation, and scheduling design under the cross-
layer control. FEC concentrates on reliable transmission pro-
vision in error-prone wireless circumstances. With adaptive
channel coding, a video stream is capable of adapting itself
to channel dynamics. A common method of adaptive channel
coding, like [39], used joint source and channel coding to
minimize the end-to-end distortion. In wireless video broad-
casting/multicasting, layered transmissions are viewed as an
effective approach to support heterogeneous receivers with
varying requirements. The work in [40] used a utility func-
tion for modeling the reception features in terms of physical
capacity, actual received bandwidth, and numbers of received
layers. Furthermore, this approach also offered layered video
transmissions through multiple video sessions. The work
in [41] employed adaptive channel coding and extended the
scalable multilayered transmissions to time-varying wireless
channels. Ji et al. [22] [43] proposed layer-adaptive videos
based on suitable rateless coding protection. In [35] and [44],
the authors devised resource allocation and scheduling strate-
gies to improve the resource utilization, including wireless
network-flow resources [43] and wireless radio resour-
ces [34]. In general, through cross-layer optimization, such as
systematic [44], application-centric [45], network-oriented,
and wireless-oriented approaches [46], the quality of video
stream can be improved. [47] was an instance of cross-layer
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FIGURE 3. Framework of classical video broadcasting to heterogeneous devices.
optimization by using the utility maximization or distortion
minimization approaches.
3) To simultaneously satisfy variable requirements and
fairly utilize limited available resources, the third cat-
egory focuses on balancing the quality of experience
among all heterogeneous users. From the view of multi-
ple users, a polling-based strategy can directly guarantee
the fairness of all the users. However, the strategy usually
presents low utilization of available resources because it
cannot adapt itself to the variety of user channels well.
As the available resource is usually constrained in the
case of multiusers, the server can improve the mini-
mal QoE of all the users and then maximizes all the
QoE by applying max-min fairness. This is one approach.
There is also another method that proportionally allocates
the resource to the users based on proportional fairness.
As video content in different scalable domains has different
rate distortions, and end-users care about the video qual-
ity rather than the bandwidth, resource allocation by using
content-based fairness is an efficient way [48]. Nonetheless,
the bottleneck in heterogeneous video broadcasting still lies
in variety, unreliability, and limited resources. This subse-
quently makes video broadcasting difficult to provide reliable
real-time video streaming for multiusers.
VI. ARCHITECTURE
In this article, we present a framework of video broad-
casting with heterogeneity support as shown in Fig. 3.
This solution considers the scenario of multicontent video
broadcasting, where videos are distributed to multiple het-
erogeneous devices. The techniques on both of the server
side and the client side are listed in the figure. The server
side includes utility-driven joint source coding/optimization,
QoE mapping, content-aware fair resource allocation,
flexible FEC, joint source/channel coding, cross-layer
optimization, layer channel coding, adaptive modula-
tion, and diversity modules. Moveover, resource-aware,
cooperative-transmission, adaptive-computing, interaction,
and QoE-capturing modules are presented on the client side.
To support heterogeneous QoE, several dynamic monitor-
ing operations are required to simultaneously serve diverse
devices with resource constraints under a heterogeneous
circumstance. Such operations correspondingly need device-
aware mechanisms from receivers, QoE-aware properties
from users, and circumstance-aware services from broadcast-
ing systems.
A. APPLICATION-LAYER CODING AND ADAPTION
QoE provision from the application layer has become the
most active and effective method in recent years. From video
source coding, a video stream is encoded into progressive
layers that have unequal importance for serving different user
groups. QoE mapping directly introduces the parameters to
video source coding such that video streams are generated
according to the requirements from users.
Scalable video sources provide more adaptability by apply-
ing a variety of schemes, such as scalable stream extraction
(e.g., [39], [49]–[52]), layer generation with different prior-
ities (e.g., [40], [53]–[55]), and summarization (e.g., [56]),
before they are dispatched to the next layers.
In broadcasting systems, it is critical to efficiently utilize
available bandwidth resources so as to provide guaranteed
quality of service for multiple users. Generally, utility is
defined as the satisfaction level of a user with respect to het-
erogeneous characteristics or defined as the utility summation
from all the users that are serviced. Since the satisfaction
102 VOLUME 4, 2016

TABLE 1. Model comparison.
is parameterized through the QoE mapping module,
developing a corresponding metric is conducive to content-
aware allocation in fair broadcasting systems.
Since layered video data are sensitive to transmission
failures, it is acceptable for servers to eliminate the retrans-
mission and lower the overhead of the unnecessary recep-
tions using FEC. To develop more flexible FEC, most of
related works focused on: 1) finding an optimal bit allo-
cation between video coding and channel coding, such
as [57] and [58]; 2) designing a new encoder for target source
rates under a given channel condition, such as [59]; 3) propos-
ing novel channel coding to achieve the required robustness,
such as low-density parity check (LDPC) [60], Turbo [61],
Reed-Solomon (RS) [62], and Fountain [63] codes; 4) creat-
ing a joint optimization framework that covers all available
error control components along with error concealment and
transmission control to improve entire system performance,
like [64].
B. PHYSICAL-LAYER CONTROL IN
CROSS-LAYER OPTIMIZATION
Besides the performance of unequal error protection, effi-
ciency improvement of transmissions in multilayers is
the major purpose of the physical layer. The remarkable
high rates with high reliability innovation include Diver-
sity Embedded Space-Time Codes (DE-STCs) [65], [66],
which allow servers to provide multiple levels of reliabil-
ity to satisfy different QoS requirements. DE-STCs real-
ized a form of communications, where the high-rate code
opportunistically took advantage of good channels and made
decisions [67]. Through cross-layer designs, joint control
with DE-STCs could benefit the diverse rates and reli-
able transmissions in a wide range of channel conditions,
especially in broadcasting/multicasting [68]–[70]. When
DE-STCs were combined with opportunistic transmissions,
the utilization of the scarce wireless resource was further
improved, particularly in variable channel conditions [71].
Current video broadcasting services are expected to pro-
vide more experience-enriched videos for consumers than
before. With the diversity of multiple devices and the vari-
able demands from mobile users, video streams are normally
initiated and delivered through multiple layered substreams.
Under the framework of cross-layer control, broadcasting
multiple video streams with multiuser QoE support can be
realized through adaptive modulation and joint diversity-
embedded high-rate reliability coding from physical layers.
C. INTELLIGENT PROCESSING ON THE DEVICE SIDE
With the increase of pervasive computing, current devices are
becoming more ubiquitous [72]. Generally, video services
on mobile devices are usually computationally intensive
and power-consuming. Consequently, emerging wireless
applications have to face a challenge of resource-constrained
video networking, such as wireless low-power surveil-
lance networks, mobile video phones, etc., because com-
putational power, memory, and batteries are limited.
However, the high resolutions and complex functionali-
ties of encoding require high resources. Thus, the video
encoder should have the capability and the scalability of
processing videos based on remaining battery capacity,
and power-scalable video encoding is a smart solution for
energy-constrained devices. This scheme performs game
theoretical analysis and models the power consumption
as a game problem. It uses game theory to solve the
tradeoff between encoding and power consumptions, and
it allows video services to work under variable energetic
constraints while keeping stable performance. Since the
user device is the direct terminal to collect the QoE, the
human perceptual method offers another approach for resolv-
ing power consumption. For example, fine-grained models,
such as perceptual macroblock-level power control based on
Just-Noticeable Distortion (JND), can adapt to available
energy resources at macroblock levels in consideration of
human perceptions. For those devices with large displays,
cooperative communications have been proven to be robust
against variable data rates [37].
VII. PRACTICAL CHALLENGES AND
COMPARATIVE RESULTS
This section provides numerical results of the performance
with a focus on the aforementioned concepts in the article.
As described above, parametric QoE models have been
proposed in the past years. Different models with vari-
ous parameters were designed for different conditions. This
section firstly gives a summary of representative models
and then compares the performance of different models.
Table 1 presents five QoE estimation models in typical video
broadcasting scenarios. Different QoSs are highlighted in the
figure.
1) Mean Perceived QoS (MPQoS) model: A qualitative
metric that was designed for CIF- or QCIF-sized videos. This
model did not consider factors in transmissions. The model
parameters were derived from video content based on [73].
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FIGURE 4. Frame-level video quality based on PSNRs, PSPNRs, and SSIMs for sequence ‘‘Crew.’’
2) Model by Ma et al.: In [74], Ma et al. have presented a
rate-and-quality model based on frame rates and quantization
step-sizes. Model coefficients were predicted by using video
features, but the transmission impairment was not modeled
herein.
3) Video Quality Metric (VQM) model: A qualitative
evaluation proposed by National Telecommunications and
Information Administration (NTIA), which consisted of the
linear combinations of features derived from several Human
Visual Systems (HVSs) [76]. The degradation introduced in
the transmission process was not evaluated herein.
4) Method by Liu et al.: Liu et al. [75] have proposed
the video quality model by considering packet losses. Loss
positions and loss severity as well as error lengths were fully
investigated in their method. The authors used a VQM based
on PSNRs (e.g., VQMp) proposed in [77] for coding artifacts.
5) Motion-based video integrity evaluation (MOVIE):
In [78], a video qualitative evaluation was presented for mod-
eling not only spatial and temporal domains but also spatio-
temporal domains. The analysis was carried out by evaluating
motion quality along computed motion trajectories.
6) Approach by You et al. : In this method [79], You et al.
developed an attention-driven foveated quality model, which
generated the perceived representation of a video by integrat-
ing visual attentions into the foveation mechanism.
TABLE 2. Evaluation of different QoE models on LIVE database.
For fairness, the experiments on LIVE database [80] were
carried out based on all the aforementioned models except
for MPQoS as the authors did not quantitatively present how
to derive model coefficients from video content. Table 2 lists
the performance of different QoE models in terms of Pearson
Correlations (PCs), root-mean-square errors (RMSEs), and
Epsilon-insensitive RMSEs (E-RMSEs) based upon the 95%
confidence interval of the video subjective scores. As dis-
played in the table, all the leading QoE models perform well
in the LIVE database. Interestingly, there is no dominant
model, which can comprehensively consider coding artifacts,
transmission factors, and HVS-related features at the same
time.
The following section provides an overview of the perfor-
mance with support of scalable video coding. Three versions
of the same video were manually selected. They were respec-
tively designated as ‘‘high-quality level,’’ ‘‘medium-quality
level,’’ and ‘‘low-quality level’’ after processed by using the
JSVM SVC reference encoder [81].
• High-Quality Level: 704 × 576 at 30 fps, QP = 32
• Medium-Quality Level: 352 × 288 at 15 fps, QP = 38
• Low-Quality Level: 176 × 144 at 10 fps, QP = 44
Fig. 4 presents the performance of SVC with three scala-
bility dimensions. Three representative quality metrics were
used for the evaluation. The horizontal axis represents the
frame index, whereas the vertical axis respectively spec-
ifies the measurements for PSNRs, structural similarities
(SSIMs) [82], and peak signal-to-perceptual-noise ratios
(PSPNRs) [83]. These three metrics reveal a similar trend
when the video quality degrades. Furthermore, all the three
metrics present numerical losses.
FIGURE 5. Packet loss ratio of different SVC layers.
The following test simulates the layered video transmis-
sions over wireless networks with flexible FEC techniques.
Fig. 5 compares the loss ratio of different SVC layers.
In this simulation, the SVC video stream was encoded into
104 VOLUME 4, 2016

a layered structure, which included the base layer and two
enhancement layers. The base layer used QCIF formats at
7.5Hz, whereas the first enhancement layer applied the QCIF
format at 15Hz, and the second enhancement layer was
based on CIF formats at 30Hz. To ensure that users could
browse the basic quality version of the video, the enhanced
FEC was implemented to protect the base layer. The second
enhancement layer was used for the comparison, so it was not
protected by FEC because of least importance. Judging from
Fig. 5, the result reveals that the packet loss ratio does not
increase dramatically until the packet error rate reaches 0.2.
When the packet error rate is between 0.3 and 0.5, the packet
loss ratios of the base layer and the first enhancement layer
are almost the same. However, the second enhancement layer
has a higher packet loss ratio than the other two layers due to
the lack of proper FEC techniques.
VIII. FUTURE RESEARCH DIRECTIONS
1) A UNIFIED QoE MEASURE MODEL
IN HETEROGENEOUS NETWORKS
As mentioned above, the heterogeneity of devices directly
influences the design of video broadcasting systems.
Although a large number of significant works on QoE under-
standing have been conducted, there is still no clear descrip-
tion of a unified QoE model for comprehensive broadcasting
even in communication systems. The intrinsic property of
video signal itself presents complex scalability, especially in
hybrid domains. After video streams are encoded and trans-
mitted, the error propagation caused by quality degradation,
packet losses, delay, format inconformity, etc., is difficult to
evaluate. Nevertheless, to make the devices and inner video
services more ubiquitous, new interactive techniques should
be developed. Subjective quality assessment in laboratory
environments is losing its relevance to realistic user termi-
nals [84]. How to combine the QoE with user background,
emotions, behavior, habits and social influences is still an
open topic.
2) DEVICE- AND USER-AWARE ADAPTIVE
JOINT CODING MODEL
In typical, an entire video stream is initiated after it is divided
into multilayered substreams to ensure the diversity of multi-
ple devices and to satisfy the various demands from users.
Following the initiation, these substreams are distributed
and transmitted through multiple subchannels in parallel to
diverse end-users. Finding a way to transmit these substreams
with support of multiuser experience has a major impact on
performance. However, in current user-centric broadcasting
systems, video coding, channel coding, and joint coding
should develop adaptability and robustness to cope with mass
interactions under ubiquitous environments. Thus, how to
intelligently, dynamically, and cooperatively encode and pro-
tect video streams so that videos can adapt themselves to vari-
able circumstances with limited resources is still challenging.
3) NETWORK COGNITIVE COOPERATIVE TRANSMISSION
In homogeneous networks, the quality of network varies
with time. Since layered video data are sensitive to failures,
the broadcasting system needs joint solutions of coding and
transmissions to adapt to quality fluctuation. Nevertheless,
the access, interactive modes, user operations, and terminals
emerge diversely in heterogeneous networks. These result
in high-delay, high-cost, and mismatch-bandwidth problems.
Thus, video broadcasting faces a new challenge of how to
develop new revolutionary techniques to support ubiquitous
computing and communications.
IX. CONCLUSION
Video broadcasting to heterogeneous devices is a research
subject that requires comprehensive QoE modeling, coding
and transmission strategies with heterogeneity support. This
article firstly investigates the key concept of QoE architec-
tures by reviewing recent results from scalable video cod-
ing to heterogeneous video transmission. Finally, this study
brings the theoretical models closer to practical implementa-
tion by presenting an integrated broadcasting system. How-
ever, the community still lacks revolutionary techniques.
Developing effective methodologies will need interdisci-
plinary efforts from academia and industry in the research
field of video coding, multiuser communication and broad-
casting networks.
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BO-WEI CHEN (M’14) received the Ph.D. degree
in electrical engineering from National
Cheng Kung University (NCKU), Tainan, Taiwan,
in 2009. Until 2010, he was a Post-Doctoral
Research Fellow with the Department of
Electrical Engineering, NCKU. He is currently a
Post-Doctoral Research Fellow with the Depart-
ment of Electrical Engineering, Princeton Univer-
sity, Princeton, NJ, USA. His research interests
include big data analysis, machine learning, social
network analysis, audiovisual sensor networks, semantic analysis, and video
encoders. He serves as the Chair of the Signal Processing Chapter of the
IEEE Harbin Section.
WEN JI (M’09) received the M.S. and
Ph.D. degrees in communication and information
systems from Northwestern Polytechnical Univer-
sity, Xi’an, China, in 2003 and 2006, respectively.
From 2007 to 2009, she was a Post-Doctoral
Research Fellow with the Institute of Comput-
ing Technology, Chinese Academy of Sciences,
Beijing, China, where she was an Assistant
Professor from 2009 to 2010 and is currently an
Associate Professor. Her research areas include
video communication and networking, video coding, channel coding, infor-
mation theory, optimization, network economics, and pervasive computing.
She is the Vice Chair of the Signal Processing Chapter of the IEEE Harbin
Section.
FENG JIANG received the B.S., M.S., and
Ph.D. degrees in computer science from the Harbin
Institute of Technology (HIT), Harbin, China,
in 2001, 2003, and 2008, respectively. He is cur-
rently an Associate Professor with the Department
of Computer Science, HIT, China. His research
interests include computer vision, pattern recog-
nition, and image and video processing. He is the
Secretary of the Signal Processing Chapter of the
IEEE Harbin Section.
SEUNGMIN RHO received the M.S. and
Ph.D. degrees in computer science from Ajou
University, Korea, in 2003 and 2008, respectively.
In 2008 and 2009, he was a Post-Doctoral
Research Fellow with the Computer Music Lab-
oratory, School of Computer Science, Carnegie
Mellon University. He was a Research Professor
with the School of Electrical Engineering, Korea
University, from 2009 to 2011. In 2012, he was an
Assistant Professor with the Division of Informa-
tion and Communication, Baekseok University. He was a Faculty Member
with the Department of Multimedia, Sungkyul University, Korea, in 2013.
He is currently an Associate Professor with the Department of Computer
Engineering, Mevlana University, Konya, Turkey. His current research inter-
ests include database, big data analysis, music retrieval, multimedia systems,
machine learning, knowledge management, and computational intelligence.
VOLUME 4, 2016 107

QoE-enabled big video streaming for large-scale heterogeneous clients and networks in smart cities

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QoE-enabled big video streaming for large-scale heterogeneous clients and networks in smart cities