Wireless Multi-view Video Streaming with
Subcarrier Allocation by Frame SignificanceTakuya Fujihashi∗ , Shiho Kodera† , Shunsuke Saruwatari† , Takashi Watanabe∗∗ Graduate School of Information Science and Technology, Osaka University, Japan† Faculty of Informatics, Shizuoka University, Japan

Abstract—When an access point transmits multi-view video
over wireless networks with multiple subcarriers, errors occur
in low quality subcarriers. The errors cause a significant degradation of video quality. The present paper proposes Significancebased Multi-view Video Streaming with Subcarrier Allocation(SMVS/SA) for the maintenance of high video quality. SMVS/SA
transmits a significant video frame with a high quality subcarrier
to minimize the effect of the errors. Evaluations using MERL’s
benchmark test sequences reveal that SMVS/SA achieves a slightdegradation of video quality. For example, SMVS/SA improves
video quality by 8.0 [dB] compared to standard H.264/AVC MVC
when the maximum packet loss ratio of each subcarrier is 10 %.

I.

I NTRODUCTION

With the progress of wireless technology and video coding
technology for multi-view video, the demand of watching
3D video on wireless devices increases [1]. To satisfy thedemand, the wireless technique and multi-view video coding
technique have been studied independently. The typical studies
of multi-view video coding are Multi-view Video Coding(MVC), Interactive Multi-view Video Streaming (IMVS) [2],User dependent Multi-view video Streaming (UMS) [3], and
UMS for Multi-user (UMSM) [4]. These studies focus on the
reduction of video traffic by exploiting the correlation of time
domain and inter-camera domain of video frames.
In view of wireless networks, Orghogonal Frequency Division Multiplexing (OFDM) [5] is used in modern wirelesstechnology. OFDM decomposes a wideband channel into a setof mutually orthogonal subcarriers. A sender transmits multiple
signals simultaneously at different subcarriers over a single
transmission path. On the other hand, the channel gains acrossthese subcarriers are usually different, sometimes by as muchas 20 [dB] [6]. The low channel gains induce low packet
reception rate at a receiver.
When a video encoder simply transmits multi-view video
over wireless network by OFDM, bit errors occur in video
transmission of low channel gain subcarriers. If these errors
occur randomly in all video frames, video quality at a user
node suddenly degrades due to 2D error propagation [7].
To minimize the effect of 2D error propagation, the present
paper proposes Significance based Multi-view Video Streaming with Subcarrier Allocation (SMVS/SA) for multi-view
video streaming over wireless networks. SMVS/SA achieves
the reduction of communication delay and video traffic while
maintaining high video quality. The key feature of SMVS/SA
is to transmit significant video frames with high channel gain
subcarriers. The significant video frames have a great effecton video quality when errors occur in the video frames.
The present paper makes two contribution. First, we propose
subcarrier-gain based 2D rate distortion to predict the effectof each video frame on video quality when the video frame
is lost. Second, we propose a heuristic algorithm to decide
the allocation between video frames and subcarriers withlow computation. The allocation achieves sub-optimal 2D
rate distortion under the different subcarrier channel gains.
Evaluations using the MATLAB video encoder and MERL’s
benchmark test sequences reveal that SMVS/SA achieves onlya slight degradation of video quality.
The remainder of the present paper is organized as follows.
Section II presents a summary of related research. We present
the details of SMVS/SA in Section III. In Section IV, evaluations are performed to reveal the maintenance of video quality
for proposed SMVS/SA. Finally, conclusions are summarized
in Section V.
II.

R ELATED R ESEARCHThis study is related to joint source-channel coding and 2D
rate distortion based video streaming.
Joint source-channel coding: There are some studies about
joint source-channel coding: SoftCast [8], FlexCast [9] andParCast [10]. SoftCast exploits DCT coefficients for significance prediction of each single-view video frame. SoftCast
allocates each DCT coefficient to subcarriers based on thesignificance and channel gains of the subcarriers. SoftCast
transmits the DCT coefficients by analog modulated OFDM
symbols. ParCast extends the SoftCast’s design to MIMOOFDM. FlexCast focuses on bit-level significance of eachsingle-view video frame. FlexCast adds rateless codes to the
bit based on the significance to prevent the effect of channel
gain differences among subcarriers. SMVS/SA follows thesame motivation to jointly consider sourced compression anderror resilience. SMVS/SA extends their concepts to multiview video streaming and frame-level significance to improve
3D video delivery quality over wireless networks.
2D rate distortion based video streaming: Several studies
have been proposed for the maintenance of high video quality.
[11] introduces an end-to-end 2D rate distortion model for 3D
video to achieve optimal encoder bitrate. [11] only analyzes
for 3D video with the left and right camera. [7] proposes
the average packet loss based 2D rate distortion to analyze
the distortion with multiple cameras. [12] proposes network
bandwidth based 2D rate distortion for bandwidth constrained
channels. The basic concept of the proposed subcarrier-gain
based 2D rate distortion is based on these studies. SMVS/SA

978-1-4799-4449-1/14/$31.00 ©2014 IEEE

Videoencoder

WiredFig. 1.

Multi-viewVideo
Wired networks

Accesspoint

User
Requirednode
multi-view video
Request packet

Wireless networks withmultiple subcarriers(Different channel gains among subcarriers)

System model of multi-view video streaming over wireless network

considers the channel gain differences of subcarriers for 2D
rate distortion to maintain high video quality in practicalwireless networks.
III. S IGNIFICANCE BASED M ULTI - VIEW V IDEOS TREAMING WITH S UBCARRIER A LLOCATION (SMVS/SA)
A. Overview
There are three requirements for multi-view video streaming over wireless networks: reduction of video traffic, suppression of communication delay, and the maintenance of highvideo quality. To satisfy all of the above requirements, we
propose Significance based Multi-view Video Streaming with
Subcarrier Allocation (SMVS/SA). The key idea of SMVS/SA
is to transmit significant video frames, which have a great
effect on video quality, by high channel gain subcarriers.
Figure 1 shows a system model of SMVS/SA. Several
cameras are assumed to be connected to a video encoder by
wire, and the encoder node is connected to an access point by
wired networks. The access point is connected to a user node
by wireless networks with multiple subcarriers. The wirelessnetworks have different channel gains among the subcarriers.
The video encoder previously transmits a encoded multi-view
video sequence to the access point. The access point decodes
the received multi-view video and waits for a request packet
from the user node. The user node transmits a request packet
to the access point by OFDM. When the access point receives
the request packet, the access point encodes the decoded multiview video based on the received request packet. The accesspoint transmits the encoded multi-view video to the user node
by OFDM.
SMVS/SA consists of request transmission, video encoding, significance prediction, heuristic calculation, sorting andvideo transmission, and video decoding.
(1) Request Transmission: A user node periodically transmits
a request packet and channel state information to an accesspoint to play back multi-view video continuously. The detailsof request transmission are described in Section III-B.
(2) Video Encoding: When the access point receives therequest packet, the access point encodes a multi-view video
sequence in 1 Group of Group of Pictures (GGOP) based on
the request packet. GGOP is the group of GOP, which is theset of video frames and typically consists of 8 frames, for eachcamera. The details of video encoding are described in Section
III-C.
(3) Significance Prediction: After the encoding, the encoder
predicts which video frames should be transmitted in highchannel gain subcarriers. To predict the significance of each

video frame, SMVS/SA proposes subcarrier-gain based 2D rate
distortion. The details of significance prediction are describedin Section III-D.
(4) Heuristic Calculation: The disadvantage of the subcarriergain based 2D rate distortion is high computational complexity.
To reduce the computational complexity, SMVS/SA proposesa heuristic algorithm. The details of the heuristic algorithm aredescribed in Section III-E.
(5) Sorting and Video Transmission: The access point
allocates video frames to the subcarriers based on the predictedsignificance. After the allocation, the access point modulates
the allocated video frames by OFDM and transmits the modulated video frames to the user node. The details of sorting andvideo transmission are described in Section III-F.
(6) Video Decoding: When the user node receives the OFDM
modulated video frames, the user node decodes the video
frames by standard H.264/AVC MVC decoder. After the decoding, the user node plays back multi-view video on display.
The details of video decoding are described in Section III-G.
B. Request Transmission
A user node transmits a request packet to an access point
when the user begins to watch multi-view video or receives
video frames in 1 GGOP. Each request packet consists of threefields: watched camera ID, required camera ID, and Channel
State Information (CSI). The watched camera ID is an 8-bit
field that indicates the camera ID being watched by the user.
The required camera ID is arrays of an 8-bit field that indicatesthe camera IDs required by the user. The CSI field is basedon 802.11n Channel State Information packet [13]. The CSI
describes the channel gain, which is Signal-to-Noise Ratio(SNR), of RF path between the access point and the user node
for all subcarriers. The CSI is reported by the 802.11 Network
Interface Card (NIC) in a format specified by the standard.
When the access point receives the request packet, the accesspoint knows the recent channel gain of each subcarrier withhigh accuracy.
C. Video Encoding
After the access point received the request packet, theencoder encodes multi-view video based on the watched andrequired camera ID fields in the request packet.
The access point encodes an anchor frame of the watchedcamera into I-frame and the subsequent video frames into Pframes. I-frame is a picture that is encoded independent fromother pictures. P-frame encodes only the differences from anencoded reference video frame and has lower traffic than Iframe. After encoding the video frames of watched camera,
the access point encodes video frames of the required cameras.
The anchor frames of the required cameras are encoded
into P-frame using the same time anchor frame in the previous
camera. The subsequent video frames are also encoded into Pframes. To encode a subsequent video frame, the access point
selects two encoded video frames that previous time frameof the same camera and the same time frame in the previous
camera. The access point tries to encode the subsequent video
frame using each encoded video frame and calculate thedistortion of video encoding. The access point decides thereference video frame of the subsequent video frame from twovideo frames. The reference video frame achieves the lowest
distortion of video encoding. After the video encoding of all
video frames in 1 GGOP, the access point gets bit streams ofeach video frame.
D. Significance Prediction
After encoding, the access point predicts the significanceof each video frame. To predict the significance, the present
paper proposes subcarrier-gain based 2D rate distortion. The
subcarrier-gain based 2D rate distortion predicts the effect ofeach video frame on video quality when the communicationof the video frame is failed. The access point maintains highvideo quality under different channel gains of subcarriers bymeans of calculating the minimum 2D rate distortion asarg min DGGOP (P )P

(1)where P is Ncamera × NGOP matrix of packet reception ratio.
The minimum 2D rate distortion reveals which video framesshould be transmitted by the high channel gain subcarriers tomaintain high video quality. Denote by Ncamera and NGOP
the number of required cameras and the length of each GOP,
respectively.
Assumption: Ncamera NGOP is the number of video frames
in 1 GGOP and is smaller than the number of subcarriersin OFDM. At the user node, SMVS/SA assumes that aproper error concealment operation is performed on lost video
frame. Generally, the error concealment operation resorts toeither temporal or inter-camera concealment. For simplicity,
SMVS/SA performs the error concealment operation for avideo frame when errors occur in bits of the video frame.
Consequently, the packet reception ratio is equivalent to theframe reception ratio.
Definition: Let DGGOP (P ) be the overall subcarrier-gain
based 2D rate distortion in 1 GGOP at the user node.
DGGOP (P ) is defined as encoding-induced distortion andnetwork-induced distortion, denoted by Dencoding (s, t) and
Dnetwork (P, s, t). They are expressed as:
DGGOP (P ) =Ncamera
GOP
 N
s=1

Dencoding (s, t) + Dnetwork (P, s, t)t=1

Dencoding (s, t) = E{[Fi (s, t) − F̂i (s, t)]2 }

(2)
(3)

Dnetwork (P, s, t) = p(s, t)Dsuccess (s, t)+(1−p(s, t))Dloss (s, t)(4)where Fi (s, t) is the original value of pixel i in M (s, t),F̂i (s, t) is the reconstructed values of pixel i in M (s, t) at
the encoder, and p(s, t) ∈ P is the packet reception ratio for
the frame at camera s and time t. The value of p(s, t) is basedon the channel gain of a subcarrier. Denote by M (s, t) the
frame at camera s and time t. Moreover, E{·} denotes the
expectation taken over all the pixels in frame M (s, t).
As can be seen from equation (3), encoding-induced
distortion refers to the Mean Square Error (MSE) between
the original frame and the reconstructed video frame at theencoder. The network-induced distortion consists of the distortion when communication is successful and failed, denotedby Dsuccess (s, t) and Dloss (s, t), respectively. Dsuccess (s, t) isexpressed as:
Dsuccess (s, t) = E{[F̂i (s, t) − F̃i (s, t)]2 }(5)

where F̃i (s, t) is expressed according to the type of video
frame and the reference video frame as:⎧⎪if M(s,t) = I-frame⎨F̂i(s,t)F̃i (s, t) = F̂p(i) (s − 1, t) else if Fp(i) ∈ M (s − 1, t).
⎪⎩F̂ (s, t − 1) else.
p(i)(6)
where p(i) is the index of the matching pixel in the reference
video frame.
On the other hand, Dloss (s, t) is expressed as:
Dloss (s, t) = E{[F̂i (s, t) − F̆i (s, t)]2 } + Dprevious(7)

where F̆i (s, t) is expressed according to the reference video
frame as:F̂conceal(i) (s − 1, t) if F̂conceal(i) ∈ M (s − 1, t).
F̆i (s, t) =F̂conceal(i) (s, t − 1) else.
(8)
where conceal(i) is the index of the matching pixel inthe reference video frame for error concealment operation.
Dprevious (s, t) is based on a reference video frame of M (s, t)
for the error concealment operation. When M (s, t) exploits
the previous time frame of the same camera as the reference
video frame, Dprevious (s, t) is expressed as:
Dprevious (s, t) = Dnetwork (P, s, t − 1)

(9)

When M (s, t) exploits the same time frame in the previous camera as the reference video frame, Dprevious (s, t) isexpressed as:
Dprevious (s, t) = Dnetwork (P, s − 1, t)(10)

E. Heuristic Calculation
The minimum subcarrier-gain based 2D rate distortion
reveals which video frames should be transmitted by thehigh channel gain subcarriers. However, the computational
complexity of network-induced distortion is high. Specifically, an access point calculates the minimum networkinduced distortion, which is equation (4), from all combinations of the subcarriers and the video frames in 1 GGOP.
As the result, the computational complexity of equation (4)is O([Ncamera NGOP ]!). To calculate sub-optimal networkinduced distortion with low computation, SMVS/SA proposesa heuristic algorithm. The heuristic focuses on the feature of
the multi-view video coding technique: the video quality of asubsequent video frame suddenly degrades when the reference
video frame is lost. Therefore, the heuristic first allocates ahigh channel gain subcarrier for early reference video frames
to prevent the degradation of subsequent video frames.
We explain the details of the proposed heuristic. An accesspoint selects I-frame and the highest packet reception ratio pin Psubcarriers . Psubcarriers is a set of packet reception ratio
in each subcarrier. The packet reception ratio is calculated by

the channel gain of the subcarrier. The access point sets p to
P (s, t) which s and t are the same frame indexes of I-frame
and removes p from Psubcarriers .
Next, the access point selects n P-frames of the I-frame’s
neighborhood and the same number of high packet reception
ratio pn in Psubcarriers . The access point calculates the sumof proposed 2D distortion of each P-frame using each pn
from equation (4), and decides the best allocation of pn which
achieves the lowest distortion. The access point sets each pn
to P which is the same frame indexes of the allocated Pframe and removes each pn from Psubcarriers . The accesspoint selects m P-frames of the previously selected P-frame’s
neighborhood and the same number of high packet reception
ratio pm in Psubcarriers . The access point also calculates thesum of proposed 2D distortion of each P-frame using eachpm from equation (4), and decides the best allocation of pm
which achieves the lowest distortion. The access point repeats
the heuristic for all video frames in 1 GGOP. The heuristic
reduces the computation to O([NGOP − Ncamera] · Ncamera !)2
when NGOP > Ncamera.
Otherwise,
thecomputation
saturates2
at approximately O([2NGOP ]!).
F. Sorting and Video Transmission
After the significance prediction, the access point allocates
bit streams of each video frame to subcarriers based on
the prediction and transmits the bit streams to a user node
over wireless networks by OFDM. The bit streams in eachsubcarrier are modulated equally, using BPSK, QPSK, QAM16, or QAM-64, with 1, 2, 4 or 6 bits per symbol, respectively.
The modulated symbols in each subcarrier are modulated by
1 OFDM symbol. The access point inserts up to 44 OFDM
symbols into 1 video packet and transmits the video packets
to the user node.
After the packet transmission, the access point transmits
EoG (End of Group of Pictures) packet to the user node. Whenthe user node receives EoG packet, the user node transmits
request packet to the encoder.
G. Video Decoding
When a user node receives an EoG packet, the user
node starts demodulation and multi-view video decoding forreceived video packets. The demodulator converts each subcarrier’s symbols into the bits of each bit stream from constellations of several different modulations (BPSK, QPSK, QAM16, QAM-64). The access point assembles the demodulated
bit streams in respective subcarriers. The subcarrier-based
assembled bit streams are equivalent to the bit streams of each
video frame. Next, the user node decodes the subcarrier-based
assembled bit streams using the standard H.264/AVC MVC
decoder. If bit streams in a video frame have errors, the user
node exploits error concealment operation. Finally, the user
node plays back multi-view video on display.
IV.

E VALUATION

A. Evaluation Settings
To evaluate the video quality of SMVS/SA, we implemented the SMVS/SA encoder/decoder with MATLAB. The
evaluation used a multi-view video test sequence: “Ballroom”(faster motion). The size of the video frames was 144 × 176
pixels for all evaluations. The test sequence was provided
by Mitsubishi Electric Research Laboratories (MERL) [14].
The number of cameras was eight. The video frames ofeach camera were encoded at a frame rate of 15 [fps]. The
GOP length of each sequence was set to eight frames. We
used 250 frames per sequence for all of the evaluations. The
Quantization Parameter (QP) value used in our experiment was
25.
The evaluation assumes that one access point and oneuser node were connected by wireless network with multiple
subcarriers. The number of subcarriers was the same as thenumber of video frames in 1 GGOP. The evaluation assumed
that request packet and bit streams of encoded I-frame arereceived error-free because these data were transmitted in thehighest channel gain subcarrier.
We evaluated the video quality of three encoding/decoding
schemes: H.264/AVC MVC, SMVS/SA w/o Significance Prediction, SMVS/SA.
1) H.264/AVC MVC: H.264/AVC MVC encodes multi-view
video exploiting the time domain and inter-view domain
correlation of video frames. The access point transmits each
encoded video frame using all subcarriers. H.264/AVC MVC
is a baseline for performance with the simplest scheme.
2) SMVS/SA w/o Significance Prediction: SMVS/SA w/o
Significance Prediction transmits each encoded video frameby randomly allocated subcarriers. SMVS/SA w/o SignificancePrediction is a baseline for performance with subcarrier allocation of the proposed approach.
3) SMVS/SA: As shown in Section III, SMVS/SA is the
proposed approach. SMVS/SA predicts the significance ofeach video frame by the proposed subcarrier-gain based 2D
rate distortion. After the prediction, SMVS/SA allocates each
encoded video frame to subcarriers based on the prediction
and transmits the video frames over wireless networks.
We used the standard peak signal-to-noise ratio (PSNR)
metric to evaluate multi-view video quality in 1 GGOP.
PSNRGGOP represents the average video quality of multiview video in 1 GGOP as follows:
PSNRGGOP = 10log10(2L − 1)HNcamera NGOP W
DGGOP

(11)

where DGGOP is the measured 2D rate distortion in 1 GGOP,
H and W are the height and width of a video frame, respectively. Moreover, L is the number of bits used to encode pixel
luminance, typically eight bits.
B. Baseline Performance
We compared the computational complexity reduction of
the proposed 2D rate distortion for greedy and proposed
heuristic calculation. We measured the computation of the
network-induced distortion, which is equation (4), for eachcalculation and plotted the logarithm of the computation.
Figure 2 shows the logarithm of the computational in 1
GGOP as a function of the number of cameras. Figure 2 showsthat as the number of cameras increases, the computationof greedy calculation increases exponentially. The greedy

Second, SMVS/SA w/o Significance Prediction achieves
higher video quality compared to H.264/AVC MVC. The
scheme transmits a video frame to the decoder by a subcarrier.
If channel gains of the subcarrier is high, the communication
is successful, and vice-versa. As the result, SMVS/SA w/o
Significance Prediction decreases the effect of low channel
gain subcarriers.

Logalithm ofcomputational complexity

90
75
60
45
30
15
0
0

1

2

3

4

5

6

7

8

Number of cameras
GreedyProposed Heuristic

Fig. 2.

Logarithm of computational complexity vs number of cameras

Third, H.264/AVC MVC has the lowest video quality of
three encoding/decoding schemes. This is because H.264/AVC
MVC transmits a video frame over wireless networks using all
subcarriers. If an error occurs in subcarrier communication, thevideo frame is lost even when the other subcarrier communication is successful. The frame loss induces 2D error propagation
among cameras and low video quality.

40

PSNR [dB]

V.
35

30

25
1

5
10
Maximum packet loss ratio [%]H.264/AVC MVC
SMVS/SA w/o Significance Prediction
SMVS/SA

Fig. 3.

PSNR vs. Packet loss ratio

calculation calculates the best combinations between video
frames and subcarriers for the high video quality maintenance.
However, the enormous computation induces high overheads
for significance estimation.
C. Comparison
We compared the video quality to evaluate the maintenanceof high video quality for the three encoding/decoding schemes
described in Section IV-A.
We implemented the three encoding/decoding schemes on
MATLAB video encoder. The MATLAB video encoder allocated encoded bit streams to subcarriers based on each encoding/decoding scheme. The packet loss ratio of each subcarrier
was a random rate r between 0 and pmax [%]. pmax was the
maximum packet loss ratio. After the allocation, the MATLAB
video encoder transmitted the bit streams by OFDM. When anerror occurred in subcarrier communication, MATLAB video
decoder exploited error concealment operation to compensate
the error. We performed 1,000 evaluations and obtained theaverage video quality.
Figure 3 shows the video quality as a function of packet
loss ratio. Figure 3 shows the following:First, SMVS/SA achieves the highest video quality of thethree encoding/decoding schemes even when packet loss ratio
increases. For example, SMVS/SA maintains video quality
by 8.0 [dB] compared to H.264/AVC MVC and 3.4 [dB]compared to SMVS/SA w/o Significance Prediction when thepacket loss ratio is 10 %. SMVS/SA transmits significant video
frames in high channel gain subcarriers to minimize the effectof 2D error propagation.

C ONCLUSION

The present paper proposes SMVS/SA for multi-view video
streaming over wireless networks with multiple subcarriers.
SMVS/SA maintains high video quality by transmitting significant video frames in high channel gain subcarriers. Evaluations
reveal that SMVS/SA enables a small degradation in video
quality.
R EFERENCES[1] M. Tanimoto and K. Suzuki, “Global view and depth (GVD) format for
FTV/3DTV,” in Three-Dimensional Imaging Visualization And Display,
2013, pp. 1–10.
[2] Z. Liu, G. Cheung, and Y. Ji, “Unified distributed source coding frames
for interactive multiview video streaming,” in IEEE ICC, 2012, pp.
2048–2053.
[3] Z. Pan, M. Bandai, and T. Watanabe, “A user dependent scheme formulti-view video live streaming,” Journal of Computational Information
Systems, vol. 9, no. 4, pp. 1439–1448, 2013.
[4] T. Fujihashi, Z. Pan, and T. Watanabe, “UMSM: a traffic reduction
method on multi-view video streaming for multiple users,” IEEE
Transactons on Multimedia, vol. 16, no. 2, pp. 228–241, 2014.
[5] O. Edfors, M. Sandell, and J. J. V. D. Beek, “OFDM channel estimation
by singular value decomposition,” IEEE Transactions on Communications, vol. 46, no. 1, pp. 931–939, 1996.
[6] D. Halperin, W. Hu, A. Sheth, and D. Wetherall, “Predictable 802.11
packet delivery from wireless channel measurements,” in ACM SIGCOMM, 2010, pp. 1–12.
[7] Y. Zhou, C. Hou, W. Xiang, and F. Wu, “Channel distortion modelingfor multi-view video transmission over packet-switched networks,”
IEEE Transactions on Circuits and Systems for Video Technology,
vol. 21, no. 11, pp. 1679–1692, 2011.
[8] S. Jakubczak, H. Rahui, and D. Katabi, “One-size-fits-all wirelessvideo,” in ACM HotNets, 2009, pp. 1–6.
[9] S. T. Aditya and S. Katti, “FlexCast: Graceful wireless video streaming,”
in ACM MOBICOM, 2011, pp. 277–288.
[10] L. X. Lin, H. Wenjun, P. Qifan, W. Feng, and Z. Yongguang, “Parcast:Soft video delivery in MIMO-OFDM WLANs,” in ACM MOBICOM,
2012, pp. 233–244.
[11] A. S. Tan, A. Aksay, G. B. Akar, and E. Arikan, “Rate-distortion
optimization for stereoscopic video streaming with unequal error protection,” EURASIP Journal on Advances in Signal Processing, vol. 2009,no. 7, pp. 1–14, 2009.
[12] J. Chakareski, “Transmission policy selection for multi-view content
delivery over bandwidth constrained channels,” IEEE Transactions on
Image Processing, vol. 23, no. 2, pp. 931–942, 2014.
[13] IEEE Standard 802.11n, Enhancements For Higher Throughput, 2009.
[14] ISO/IEC JTC1/SC29/WG11, Multiview Video Test Sequences from
MERL, 2005.