This paper proposes an adaptive combined scalable video coding (CSVC) system for video transmission over MIMOOFDM (MultipleInput MultipleOutputOrthogonal Frequency Division Multiplexing) broadband wireless communication systems. The scalable combination method of CSVC adaptively combines the medium grain scalable (MGS), the coarse grain scalable (CGS) and the scalable spatial modes with the limited feedback partially from channel state information (CSI) of MIMOOFDM systems. The objective is to improve the average of peak signaltonoise ratio (PSNR) and bit error rate (BER) of the received video stream by exploiting partial CSI of video sources and channel condition. Experimental results show that the delivered quality using the proposed adaptive CSVC over MIMOOFDM system performs better than those proposed previously in the literature.
1. Introduction
T
he development of telecommunication technology and multimedia services require increasingly broadband data transmission. In particular, raising transmission data rate in mobile video services is the key solution to meet with market demands. The demands are realized with the technologies of 4G and beyond 4G (5G), which adopt MIMOOFDM (multiple input multiple outputorthogonal frequency division multiplexing).
In multimedia services, video communication plays an important role because it consists of various interrelated information. In video transmission, realtime service is one of the most challenging problems. The development of video communication in wireless networks depends on the following conditions: (a) a userspecified bandwidth based on the transmission quality; (b) an effective video encoder/decoder with the minimum bit error rate (BER). To satisfy (a), MIMOOFDM system can provide broadband services
[1]
[2]
. For (b), the method of
scalable video coding
(SVC) can divide a bitstream into several subbitstreams or layers according to network states, which can offer efficiency and superior quality video coding on broadband services compared to other video coding methods
[3]
[4]
. There are two groups of the bitstream, one is the baselayer bitstream and the other contains some enhancementlayer bitstreams. Combination of baselayer and several enhancementslayers in decoder system will produce a high quality video, that is scalable technique of
combined scalable video coding
(CSVC). When an interruption or error occurs in video transmission, at least the baselayer can be received by the receiver
[3]
.
Applications of MIMOOFDM have been rapidly developed since proposed in
[5]
. Previous studies confirm that the application of MIMOOFDM in the field of video transmission and multimedia telecommunication systems can be implemented. In
[6]
, SVC method has been used with MIMOOFDM, although the structure of the SVC is still commonly used. Zheng
et al
. proposed a novel scheme that integrates multiple description coding (MDC) by the structure of spacetime code in video transmission over MIMOOFDM systems in
[7]
. In
[8]
, SVC was developed for multiuser MIMOOFDM systems. Alternatively, other research on SVC over WiMAX system were proposed
[9]
[10]
. Moreover, there are also some researches on SVC over MIMO system without OFDM
[11]
.
The utilization of CSI (channel state information) in MIMOOFDM, either completely or partially has been applied to SVC in telecommunication system, aimed to create reliable and adaptive communication systems. The CSI can be completely or partially known at the transmitter and receiver sides. Sometimes, only statistical information on the channel state may be available. The adaptation of MIMOOFDM system mainly focuses on a rapidly changing environment
[12]
. By using CSI, an adaptive channel selection system was proposed in
[11]
and an adaptive antenna selection system was shown in
[13]
. The exploitation of such channel information is conducted to increase system performance and reduce hardware complexity
[14]
. In practice, however, full CSI may not be directly available due to feedback overhead and feedback delay. In general, a transmitter does not have direct access to its own CSI. Therefore, some indirect means are required for the transmitter. In particular, CSI for the timevarying channel cannot be tracked completely by the transmitter and thus only partial information can be exploited
[15]
. In
[16]
, the authors propose using the crosslayer design framework for efficient broadcasting using CSI in SVC over MIMOOFDM, where a subcarrier allocation strategy is used to assign transmission channels for different users. The authors of
[17]
investigate an optimal solution for adaptive H.264/SVC video transmission over MIMO channels using CSI. The work in
[18]
proposes a novel joint H.264/SVC with rate control algorithm using CSI for video compression and transmission over MIMO systems. However, the approaches above have not exploited the potential of CSI in wireless video transmission over MIMOOFDM systems for adaptive CSVC. Moreover, the application of MIMOOFDM precoding method has not been used widely compared with antennaselection method. In this work, we will mainly consider the precoding technique according to the making of adaptive systems in CSVC from MIMOOFDM to improve PSNR and BER of the received video stream.
This paper is different from previous papers in two aspects. First, this paper exploits the potency of partial CSI over closedloop precoding MIMOOFDM for video transmission using SVC which is part of H.264/AVC standard with Joint Scalable Video Model (JSVM) test model
[19]
[20]
. Second, the adaptive characteristic of the CSVC encoder is based on the medium grain scalability (MGS), the coarse grain scalability (CGS) and the spatial scalability by using the same matrix from the partial CSI precoding MIMOOFDM. MGS and CGS modes are the facilities of JSVM used to improve video coding for the efficiency and quality of signaltonoise ratio (SNR)
[4]
.
In this paper, we propose a new scheme of adaptive CSVC for wireless video transmission by partial CSI in MIMOOFDM systems. The receiver chooses a matrix from a codebook based on current channel conditions and conveys the optimal codebook matrix to the transmitter, where an errorfree and zerodelay feedback channel is assumed. This research is more focused on the adaptive CSVC method using limited feedback as further development of our previous work
[21]
[22]
. In
[21]
, the testing scheme conducted in CSVC does not use yet CSI method in MIMOOFDM systems, whereas in
[22]
it is only CSVC used in the scheme of WLAN (IEEE 802.11e standard) without MIMOOFDM systems in the testing using Network Simulator II (NS2). The main contribution of this paper compared to
[21]
[22]
is the use of Adaptive CSVC with the method of limited feedback of CSI scheme in MIMOOFDM systems.
To the best of our knowledge, an adaptive CSVC scheme that uses the limited feedback of partial CSI to enhance the quality of video streaming through MIMOOFDM systems has not been studied yet. In this work, we compare the adaptive CSVC with nonadaptive SVC based on modes of MGS, CGS and spatial scalability. The application of this scheme improves BER and average peak signaltonoise ratio (PSNR) performance in the system. Furthermore, the system is robust towards noise changes in the transmission channel.
Our contributions are as follows.

• We exploit and analyze the potential of partial CSI over closedloop precoding MIMOOFDM for video transmission using CSVC. The adaptive characteristic of the CSVC encoder is based on the medium grain scalability (MGS), the coarse grain scalability (CGS) and the spatial scalability by using the same matrix from the partial CSI precoding MIMOOFDM.

• We propose a new scheme of adaptive CSVC for wireless video transmission by partial CSI in MIMOOFDM systems. The receiver chooses a matrix from a codebook based on current channel conditions and conveys the optimal codebook matrix to the transmitter, where encoder of CSVC makes choice of three conditions depending on CSI.

• We demonstrate effectiveness of our proposed scheme through computer simulation. The experimental results confirm that the MGS mode performs significantly better compared to the CGS and Spatial mode scheme, and show that the delivery quality improves average peak signaltonoise ratio (PSNR) and bit error rate (BER) performance over those previously proposed work. We also show how the scheme is robust towards the changes in the value of additive white Gaussian noise (AWGN). Adaptability of the proposed scheme is evaluated based upon PSNR and BER performance in fluctuating channels. The performance of the adaptive CSVC scheme with partial CSI is better than nonadaptive CSVC system (without CSI); more precisely, the results in the MGS mode have a gain of 2.7 – 3.1 dB for YPSNR and 0.5  1.5 dB improvement in signaltonoise ratio (SNR) at BER of 103. The proposed scheme is a promising approach for wireless video transmission on broadband services in the future.
The following sections of this paper are organized as follows. Section 2 presents a description of the system including CSVC, MIMOOFDM systems using partial CSI feedback. A new proposed algorithm for adaptive schemes CSVC is presented in Section 3; meanwhile Section 4 describes the results and discussion. Finally, conclusions are presented in Section 5.
2. System Descriptions
In this section, we introduce how to produce SVC and CSVC from JSVM test model of H.264/AVC standards, some concepts of partial CSI in MIMOOFDM systems, and then describe system model of the proposed framework scheme on adaptive CSVC.
 2.1 CSVC
There are three basic types of scalability: (a) quality (SNR) scalability, (b) spatial scalability and (c) temporal scalability. A SVC system consists of encoder and decoder shown in
Fig. 1
(a) and (b), respectively. In the encoder system, there are two groups; one is the baselayer bitstream and the other contains some enhancementlayer bitstreams. The baselayer bitstream contains vital information and the enhancementlayer bitstreams have the residual additional information to improve the transmitted video quality according to network conditions and user demands.
Scalable video encoding and decoding systems
The combinedscalability is an appropriate solution because of the diversity in the characteristics of the input or video input, fluctuating network conditions, and multi terminals on the network
[23]
. Some researches employ several layers of combinedscalability, including one baselayer and some enhancementlayers. In
[25]
, the authors proposed a system whose encoder contains three layers of combined scalability as shown in
Fig. 2
. Frequently used CSVC mode are medium grain scalable (MGS), coarse grain scalable (CGS) and spatial scalability, which we can choose based on efficiency, characteristics, and the computation of the systems
[23]
[24]
[25]
[26]
.
The Structure of an encoder containing three layer CSVC
 2.2 Limited Feedback Method using Partial CSI over MIMOOFDM
Spacetime block code (STBC) is a technique of wireless communication to transmit multiple copies of data/bitstream over fading channels
[1]
[27]
. MIMOOFDM technology, especially in multimedia communication services, has become a main issue in wireless communications
[2]
. Some researches of SVC transmission over MIMOOFDM adopt the results from the previous works, and mainly focus on the impacts of multipath fading and additive white Gaussian noise (AWGN)
[6]
[7]
[8]
.
Several limited feedback methods with closedloop method has been widely used until now. Based on information available at the transmission, there are three types of CSI methods, namely the instantaneous, statistics of the channel, and partial or quantized CSI. Partial or quantized CSI is the most practically used
[11]
[12]
[13]
[14]
[15]
[16]
[29]
. Development of partially limited CSI feedback is the topic of this research.
 2.3 System Model
The proposed framework scheme is shown in
Fig. 3
. The use of OFDM on MIMO systems
[1]
[2]
[12]
with the operation of IFFT (inverse fast Fourier transform)/FFT (fast Fourier transform) and CP (cyclic prefix) are performed at each transmitter (
x_{n}
) and receiver (
y_{n}
). The received signal
y_{i}
, which is
M_{R}
× 1 vector with the subcarrier
i
th (
i
=0, 1,…,
N
1) in MIMOOFDM system, can be represented by
[28]
[29]
as
where
ρ
denotes the signaltonoise power ratio,
x_{i}
is the signal emitted by matrix
M_{T}
× 1 which is independent with distribution of
CN
(0,1), and
z_{i}
complex Gaussian noise with a matrix
M_{R}
× 1, which is i.i.d. as
CN
(0,1). The channel frequency response is given by
where
H
(
e
^{j2πθ}
)is the
M_{R}
×
M_{T}
matrixvalued channel impulse response and
h_{l}
represents the
l
th tap (
l
=0,1,…,
L
1).
Schematic and structure of the system being considered
In the MIMOOFDM system with
N_{c}
subcarriers, the data flowing through OFDM modulators will be processed by IFFT on blocks along
N_{l}
followed by the paralleltoserial (P/S) conversion. OFDM signal is generated along the symbol
N_{l}
+
L_{cp}
emitted simultaneously from each antenna. At the receiver
R_{x}
signal passes through OFDM demodulation, first eliminating the CP and then the
N
point FFT processing. The output of the OFDM demodulation is eventually separated and decoded as shown in
Fig. 3
.
By aggregating
N_{c}
subcarriers, the vector can be derived from (1) as
Based on (1) and (3), MIMOOFDM equation can be rewritten as
Given channel gain
H
that is estimated on the receiver side (
R_{x}
) and
L
refers to the size of a codebook, the index of the corresponding codeword is chosen to represent the estimated condition of
H
. In contrast to the nature of the full CSI, the partial CSI is represented only in a set of indices that are feedback corresponding to the transmission side (
T_{x}
). Each index can be expressed by the number of bits
F_{B}
, which correspond to the total number of codeword
L
=2
^{FB}
of the codebook. If
W_{i}
is expressed as a codeword
i
th,
i
=1,2,3, ⋯,
L
, for a codebook
F
= {
W_{1}
,
W_{2}
,…,
W_{L}
}, the codeword is selected by a mapping function
f
(.). The matrix
F
is chosen by a function
f
:ℂ
^{MTxMR}
→
F
= {
W_{1}
,
W_{2}
,…,
W_{L}
}. On the condition of the channel
H
, codebook method can be represented as follows
where
W_{opt}
is the codeword that best represents
H
from the mapping function
f
(.). If
C
ϵ ₵
^{M×T}
is expressed as a spacetime codeword of length
M
, it is expressed as
where
c_{k}
= [
c
_{k,1}
c
_{k,2}
…
c_{k,M}
]
^{T}
,
k
=
1
,
2
,…,
T
, and
M
≤
M_{T}
.
In
orthogonal spacetime block code
(OSTBC)OFDM precoded systems, spacetime codeword
C
is the multiplication of a precoding matrix
W
, which is selected from the codebook
F
in (5). Assuming that the channel
M_{T}
is in a static condition for
T
symbols, the received signal
y
in (4) can be expressed as
For a given channel matrix
H
and precoding matrix
W
, the codeword is considered as the
pairwise error probability
(PEP) Pr(
C_{i}
→
C_{j}

H
). The probability of the codeword
C_{i}
is transmitted whereas
C_{j}
with
j
≠
i
is decoded. Upper bound condition of the PEP is given as
where
ρ
=
E_{x}
/
N
_{0}
is SNR,
E_{i,j}
=
C_{i}

C_{j}
on STBC scheme, and 
HWE_{i,j}

_{F}
is the Frobeniusnorm of the matrix
HWE_{i,j}
[29]
. From (8), part of
is required to be maximized in order to minimize the PEP
[15]
. This leads to the following codeword selection criterion
In the case of nondeterministic channel, the following criteria are used in the codebook design,
Regarding the minimization problem, it can be solved by using Grassmannian subspace packing
[29]
. The solution of Grassmannian packing which is based on the amount of
M_{T}
, the length of the codeword
M
, and the size of the codebook
L
is time consuming and gives an indirect solution. Therefore, the consideration of the practical completion of the suboptimal method is done by utilizing the Discrete Fourier Transform (DFT) matrix
[30]
, as shown below
The first codeword
W_{DFT}
is obtained by selecting
M
columns of the
M_{T}
×
M_{T}
matrix DFT, which the input (
k
,
l
)th given as
where
k
,
l
= 1,2,…,
M_{T}
. Thus,
θ
is a diagonal matrix
where the independent variables
have been determined. If the initial codeword WDFT, the codeword (
L
1) is obtained by multiplying
W_{DFT}
and
θ^{i}
,
i
= 1,2,...,
L
1. Independent variables
in (12) are determined by maximizing the minimum chordal distance, so that
Note that IEEE 802.16e specification for mobile WiMAX system employs this particular designed method.
Table 1
shows the values of
u
= {
u
_{1}
,
u
_{2}
,…,
u_{MT}
} that are adopted from IEEE 802.16e standard for various values of
M_{T}
,
M
, and
L
[28]
. For example, when
M_{T}
= 4,
M
= 3, and
L
= 64,
W
_{1}
is stated as
Codebook design parameters for OSTBC in IEEE 802.16e
Codebook design parameters for OSTBC in IEEE 802.16e
The remaining precoding matrices
W_{i}
are obtained from
where
i
= 2,3,4, …, 64.
3. The Proposed Algorithm
The proposed adaptive CSVC algorithm for wireless video transmission is described as follows.
 Step 1: Channel Initialization
Process in Step 1 is the initialization channel on the transmitter (
T_{x}
). Pseudocode used to estimate channel of MIMOOFDM systems are shown in
Table 2
.
Initialization channel and input video sequence
Initialization channel and input video sequence
In
Table 2
,
L
is the length of the frame,
F_{IFFT}
is the IFFT matrix,
CP
is the cyclicprefix;
T_{xb}
is the total bits sent through the channel to reach the receiver (
R_{x}
);
L_{CP}
is the length of
CP
,
L_{IFFT}
is the frame size of IFFT, and
x_{MT}
is bitstream from the transmitter.
 Step 2: Processing of Precoded STBC
Step 2 is a process that utilizes precoding STBC limited feedback CSI by using matrix
W_{opt}
.
Table 3
presents the steps to determine the
F
,
W_{i}
, and
W_{opt}
according to (11), (15), and (5).
Processing of precoded STBCOFDM
Processing of precoded STBCOFDM
 Step 3: Processing of Adaptive CSVC
In this step, the determination carried out through the adaptive process at encoder CSVC utilizes the information on limited feedback CSI from the matrix
W_{opt}
. Determination of the maximum index of the norm matrix
H
in the codebook as the Frobeniusnorm of the matrix codebook
W
and
H
is done using the function of
max
(
cal
). The corresponding subroutine is shown in
Table 4
.
The range of values of the max(cal) function are 0 ≤ max (cal) ≤ 1, which depends on the condition of the transmission channel. There are three conditions for adaptive systems, i.e. using mode MGS, CGS, and Spatial Scalability. The conditions that will be used in the method of adaptive CSVC in the encoder by using the CSI limited feedback method require two bits of feedback (
F_{B}
). The codebook
W_{opt}
and
W_{i}
can be obtained based on (5) and (15). Max(cal) function is given as
cal
(
i
) = norm[
H
×
W_{opt}
(:,:,i),’fro’] as shown in
Table 4
.
The worst case is the option of
max
(
cal
) = 0, i.e. the condition in which the transmitter (
T_{x}
) does not get CSI from receiver (
R_{x}
), so the encoder of CSVC will choose spatial scalable mode. Selection of spatial modes is based on the consideration of computational complexity from CGS and MGS modes. In the best conditions (
max
(
cal
) approaches the value 1) in which the encoder uses MGS selection mode.
Processing of adaptive CSVC
Processing of adaptive CSVC
 Step 4: Post Processing
The received signal at the receiver (
R_{x}
) in pseudocode is shown in
Table 5
, where
L
is the length of frame,
L_{MT}
is the length of data,
H_{est}
is the channel matrix of predicted results;
T_{xb}
is the total bits sent through the channel to the receiver (
R_{x}
);
σ
^{2}
is the noise variance, and
M_{R}
is the number of receivers. After all steps are completed, Step 1 is repeated for new input (video sequence).
Post processing
The approach above can also be summarized in the
Table 6
.
Summary of algorithm
4. Results and Discussions
Computer simulations were conducted to verify the performance of the proposed adaptive CSVC method. Parameters used in this research are listed in
Table 7
.
Simulation parameters
Testing and analysis of video input (City and Crew sequences) in this work are limited by only 50 frames due to the computational efficiency issues. The selection of the type of video sequence as input video is based on the characteristics of each of such sequences. In the City sequence, the movement of the camera is dominant to the background, while in the Crew sequence moving objects is dominant to the background. Peak signaltonoise ratio (PSNR) is used to objectively measure the quality between an original sequence and a reconstructed sequence. This metric depends on the Mean Squared Error (MSE) given by
and PSNR is defined as
where
W_{pix}
is number of pixel/row,
H_{pix}
is number of row/frame,
f
(
x
,
y
) is luminance intensity of pixel in the original frame,
g
(
x
,
y
) is luminance intensity of pixel in the reconstructed frame, and
n
is the number of bits per pixel.
Calculation and analysis of the bit rate is use equation is given by
where
B_{r}
as bit rate,
N_{b}
total as bit,
N_{f}
as number of frames, and
M_{f}
as mean frame rate.
We analyze the coding efficiency of encoder CSVC from City sequence (a) and Crew sequence (b) as an input as shown in
Fig. 4
. The encoding were operated according to the JSVM algorithm
[20]
. Three modes are shown in the figure, namely spatial scalable, CGS, and MGS modes. The spatial scalable mode, CGS mode, and MGS mode use two, three, and four layers, respectively. In City sequence (
Fig. 4
(a)), it shows that on layer 3, MGS mode gains YPSNR (luminance component of the video) about 2 dB above CGS mode at bit rate 1500 kbps. On the other hand, at YPSNR 42 dB, MGS mode has a gap about 400 kbps above CGS mode. On layer 2, we see that there is no significant difference, meanwhile on layer 1 there is 2.5 dB to 3 dB difference. The results for Crew sequence (
Fig. 4
(b)); show that on layer 3, MGS mode gains YPSNR about 1 dB above CGS mode at bit rate 1000 kbps. On the other hand, at YPSNR 40 dB, MGS mode has a bit rate gap about 500 kbps above CGS mode. On layer 2 and layer 1, MGS mode gains YPSNR of 0.5 – 1.0 dB above CGS mode at bit rate 250 kbps. The result shows how the coding efficiency of MGS mode improves YPSNR over the CGS and Spatial mode.
Coding efficiency comparison of mode of MGS, CGS, and Spatial scalable
Analysis of system performance according to the proposed scheme in
Fig. 3
gives the results as shown in
Fig. 5
. The average of YPSNR as output of the proposed scheme is shown in
Fig. 5
in which there are adaptive (using CSI) and nonadaptive (without CSI) systems. For adaptive system in City sequence (
Fig. 5
(a)): Spatial scalable has a value of 19.5 dB, CGS has a value of 21.2 dB, and MGS is 23.1 dB. For nonadaptive (without CSI) system: Spatial scalable has a value of 18.46 dB, CGS has a value of 18.9 dB, and MGS is 20.4 dB. Also, in the case of Crew sequence (
Fig. 5
(b)) with adaptive systems: the Spatial scalable, CGS, and MGS achieve SNR of 18 dB, 21.7 dB, and 23.6 dB, respectively. For nonadaptive systems (without CSI) in Crew sequence: the Spatial scalable, CGS, and MGS achieve SNR of 17.2 dB, 19.7 dB, and 20.5 dB, respectively. The performance of the adaptive CSVC (using CSI) is better than nonadaptive CSVC system (without CSI). More precisely, the results in the MGS mode have a gain of 2.7 – 3.1 dB for YPSNR. The use of MGS gives better result compared to CGS mode and spatial scalable mode. For comparison between the proposed systems, which adaptive CSVC is compared with nonadaptive system, it is shown that the proposed system is better than nonadaptive systems. The adaptive MGS mode has the best performance with YPSNR average is 23.1 dB in City sequence and 23.6 dB in Crew sequence. The result shows how the proposed scheme (adaptive CSVC) makes improvement on PSNR better than the nonadaptive (conventional) system.
YPSNR as function of number of frames as output of the proposed adaptive CSVC
From the results of Monte Carlo simulation analysis in
Fig. 6
, we observe that the proposed system offers the best performance on adaptive MGS mode. The adaptive schemes is shown to have better performance than nonadaptive (not use the limited feedback of CSI), such as the scheme of Alamouti
[31]
. For the value of
BER
= 10
^{3}
, the adaptive MGS mode has a gap of 1.5 dB in City sequence (
Fig. 6
(a)) and 1 dB in Crew sequence (
Fig. 6
(b)) compared to the nonadaptive MGS mode. There is a case where a spatially adaptive SNR has a gap of 1.5 dB in City sequence and 0.5 dB in Crew sequence compared with nonadaptive spatial, while the adaptive CGS mode also has a gap of 1.5 dB in City sequence and 0.5 dB in Crew sequence compared with nonadaptive CGS mode. Therefore, the performance comparison of the proposed scheme (adaptive system) is generally around 0.5  1.5 dB better than the nonadaptive system on the condition
BER
= 10
^{3}
. Based on the result, we can conclude that the proposed scheme (adaptive CSVC) can improve BER better than the nonadaptive (conventional) system.
To analyze the influence of additive white Gaussian noise (AWGN) on the system performance, we varied the variance of the noise (σ
^{2}
), by using the following equation
where SNR is SignaltoNoise Ratio,
c
is the coefficient of noise variance, and
M_{T}
is the number of transmit antennas at the transmitter. The results of the analysis, based on (19) can be seen in
Fig. 7
shown that such system proposed in the value of
BER
= 10
^{3}
has an SNR gap of 4 dB in City sequence and 3 dB in Crew sequence, where the coefficients of noise variance (
c
) are 0.5 and 1. This shows the system robustness towards the changes in the value of AWGN variance.
5. Conclusions
In this paper, we have investigated adaptive CSVC over MIMOOFDM systems with limited feedback. We present a new scheme for platform transmission video using adapted CSVC over MIMOOFDM by using limited feedback CSI, where an errorfree and zerodelay feedback channel is assumed. The important difference of the proposed adaptive schemes with other works is that the design scheme of adaptive precoding and CSVC utilizes partial CSI methods with the same codebook matrix. Evaluation and analysis of video transmission are based on the JSVM over broadband wireless networks. We also investigate the impacts of the use of MGS, CGS, and Spatial scalable modes of performance of this adapted system and show the system robustness towards the changes in the value of AWGN variance. The application of MGS mode on CSVC increases the performance compared to CGS and Spatial scalable modes. Experimental results show that the delivered quality using the proposed adaptive CSVC over MIMOOFDM system with partial CSI improves average peak signaltonoise ratio (PSNR) and bit error rate (BER) of the received video stream. In general, our scheme is implementable in video sequence of broadband wireless service.
BER performance of adaptive CSVC over MIMOOFDM systems
BER performance of adaptive CSVC for analysis of system robustness
BIO
Kalvein Rantelobo received the B.E. degree from Hasanuddin University, Makassar, Indonesia in 1996 and the M. Eng. degree from Institut Teknologi Bandung (ITB), Bandung, Indonesia in 2003. Currently, he is working towards the Dr. Eng. degree in Electrical Engineering Dept. at Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. His research areas include video transmission over mobile broadband services. He is a member of IEEE.
Wirawan received the B.E. degree in telecommunication engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia, in 1987, the DEA in signal and image processing from Ecole Supérieure en Sciences Informatiques, Sophia Antipolis, and the Dr. degree in image processing from Telecom ParisTech (previously Ecole Nationale Supérieure des Télécommunications), Paris, France, in 1996 and 2003, respectively. Since 1989 he has been with ITS as lecturer in the Electrical Engineering Department. His research interest lie in the general area of image and video signal processing for mobile communications, and recently focuses more on underwater acoustic communication and networking and on various aspects of wireless sensor networks. He is a member of IEEE.
Gamantyo Hendrantoro received the B. Eng degree in electrical engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia, in 1992, and the M. Eng and PhD degrees in electrical engineering from Carleton University, Canada, in 1997 and 2001, respectively. He is presently a Professor with ITS. His research interests include radio propagation modeling and wireless communications. He has recently been engaged in various collaborative studies, including investigation into millimeter wave wireless systems for tropical areas with Kumamoto University, Japan, implementation of digital TV systems with BPPT, Indonesia, and development of Indonesia's first educational nanosatellite (IINUSAT) together with five other universities and LAPAN. Dr. Gamantyo Hendrantoro is a senior member of the IEEE.
Achmad Affandi received the B.S. degree in electrical engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia in 1990, DEA and Dr. Eng degree from INSA de Rennes, France in 1997 and 2000, respectively. Since 2004, he has been an Associate Professor at the Dept. of Electrical Engineering, ITS. Since then, he has been engaged in research on digital mobile communication systems and applications of the marine communication systems. Dr. Achmad Affandi is a member of IEEE.
HuaAn Zhao received the B.S. and M.S. degrees in electrical engineering from Anhui University, China in 1982 and 1986, respectively. He also received the Ph.D. degree in computer science from Hiroshima University, Japan in 1993. During 19932006, he joined the faculty of engineering, Kyushu Kyoritsu University. From 2007, he is a professor in Kumamoto University. His current research interests the areas of communications, graph theory and its applications, signal processing and VLSI layout design. He is a member of IEEE.
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