# Quality-Aware Coding and Relaying for 60 GHz Real-Time Wireless Video
Broadcasting

Joongheon Kim†, Member, IEEE, Yafei Tian♮, Member, IEEE, Stefan Mangold§,
Member, IEEE, and
Andreas F. Molisch‡, Fellow, IEEE †‡Communication Sciences Institute,
University of Southern California, Los Angeles, CA 90089, USA
♮School of Electronics and Information Engineering, Beihang University,
Beijing 100191, China
§Disney Research, 8092 Zurich, Switzerland
Emails<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Wireless streaming of high-definition video is a promising application for 60
GHz links, since multi-Gigabit/s data rates are possible. In particular we
consider a sports stadium broadcasting system where video signals from
multiple cameras are transmitted to a central location. Due to the high
pathloss of 60 GHz radiation over the large distances encountered in this
setting, the use of relays is required. This paper designs a quality-aware
coding and relaying algorithm for maximization of the overall video quality.
We consider the setting that the source can split its data stream into
parallel streams, which can be transmitted via different relays to the
destination. For this, we derive the related formulation and re-formulate it
as convex programming, which can guarantee optimal solutions.

##  I Introduction

Wireless video streaming in the millimeter-wave range has received a lot of
attention in both the academic and industrial communities. In particular the
60 GHz frequency range is of great interest: around 7 GHz bandwidth (58-65
GHz) has been made available, which enables multi-Gbit/s high-definition video
streaming in an uncompressed, or less compressed, manner. Therefore, two
industry consortia, i.e., WirelessHD and Wireless Gigabit Alliance (WiGig),
have developed related specifications; there are also two activities within
the IEEE, namely IEEE 802.15.3c [1] and IEEE 802.11ad [2].

In this paper, we design and analyze such a 60 GHz video transmission system
for outdoor applications, in particular in a sports stadium. In this system,
there are multiple wireless video cameras in a stadium for high-quality real-
time broadcasting, all send their signals to a broadcasting center. To
transmit uncompressed HD video streams in real-time, a data rate of around
$1.5$ Gbit/s is required [3]. Since the distance between wireless cameras and
a broadcasting center is on the order of several hundred meters, the high
pathloss at 60 GHz is one of key challenges that limits communication ranges.
One promising approach to deal with this problem is using relays for extending
the coverage [3]. Additionally, we take the complexity of the antennas into
account. In order to compensate for the high pathloss, as well as to reduce
interference, high-gain antennas need to be employed. We also consider the
situation where the antenna at the camera (video source) can form multiple
beams, so that it can split its data stream into multiple streams and send
them to the destination via parallel links. By introducing multiple beams in
each relay, our framework operates even though the number of relays is smaller
than the number of sources. In this case, appropriate compression and routing
of multiple streams via the same relay can be used. Relaying for sum rate
maximization has been analyzed in many papers. However, for video streaming,
we are more interested in video quality. For this, the proposed quality-aware
formulation selects the relays and decides the coding rates for every single
video stream. With this formulation, optimal solutions are obtained by convex
optimization techniques.

Thus, the contribution of the proposed scheme is achieving joint rate and
relay selection with video quality consideration and an interference-free
operation. This combination of special features makes it different from other
schemes.

(a) Wireless Video Camera (Source)

(b) Relay

(c) Broadcasting Center

Figure 1: System Components (Camera (a), Relay (b), Broadcasting Center (c))
Figure 2: Overall Architecture

##  II Related Work

There are two salient factors in our broadcasting setup: (i) stream splitting
via the multiple-beam antennas, and (ii) rate control for video quality
maximization. In the following, we outline why these aspects make the setup
different from other scenarios that have been treated in the literature. There
is, of course, a huge number of papers (too numerous to reference here)
dealing with the topic of routing (relay selection) and sum rate maximization
in multi-node networks with multi-beam relays [5] and with multi-beam sources
and relays [6]. However these papers do not consider the control of the video
coding rate (compression), and are thus not directly applicable to our
scenario. For video networks, example publications include [7, 8, 9]. The
scheme in [7] is for video streaming over IEEE 802.11 networks. The proposed
scheme is efficient for the multi-hop networks it investigates; however, it
does not consider the video stream splitting via the multi-beam antennas and
route selection. Ref. [8] considers video streaming in multi-hop networks. It
considers networks similar to ours (when specialized to the two-hop case), but
again does not investigate multi-beam antennas and the splitting of the data
streams. The formulation in [9] considers path selection for video streaming
in MANET. It concentrates on the consideration of interference, a factor that
does not play a role in our 60 GHz channel, where the high directionality of
the links prevents inter-stream interference. None of these papers consider
the control of the coding rate (compression). In previous research on video
streaming, schemes usually considered multipath transmission to combat the
limited bandwidth [8][9]. Also, some of the research considered retransmission
of frames and tried to reduce transmission time [7]. However, thanks to the
extremely large bandwidth at 60 GHz, these factors are not longer critical in
our system. The representative work which considers both rate control and
routing appeared in [10]: However, the relays cannot aggregate streams, which
is required when the number of relays is smaller than the number of flows. In
addition, the proposed framework does not consider the properties of video. In
our previous work [3], we considered the properties of 60 GHz channel, rate
control, and video quality, but we restricted ourselves to the cases that the
number of relays exceeds the number of sources (i.e., no consideration for
multi-beam antennas) and the numbers of sources and destinations are
identical. We finally note that wireless video for sports stadiums [11];
however the fundamental setup differs from ours in that [11] considers content
distribution to wireless devices of the audience in the stadium, while our
system is for real-time streaming to a broadcasting center in the stadium.

##  III A Reference System Architecture

###  III-A Link Budget Analysis

Shannon’s equation for the capacity is used for the data rate:

$C=B\cdot\log_{2}\left(1+\text{SNR}\right)$ (1)

where SNR is equal to $P_{\text{signal}}/P_{\text{noise}}$ on a linear scale,
$P_{\text{signal}}$ and $P_{\text{noise}}$ stand for the signal power and
noise power, and $B$ stands for bandwidth ($2.16$ GHz in WiGig [2]) [12]. The
signal power expressed in dB, $P_{\text{signal, dB}}$, is obtained as:

$P_{\text{signal,dB}}=E+G_{r}-W-O(d)+F(d)$ (2)

where $E$ denotes the EIRP (equivalent isotropically radiated power), which is
limited to $40$ dBm in the USA and $57$ dBm in Europe. $G_{r}$ means the
receiver antenna gain and is set to $40$ dB, which corresponds to high-gain 60
GHz scalar horn antennas [13], which we propose to achieve long range.
Shadowing can be either temporally variant (due to people walking close to
LOS), or time-invariant (due to objects that are (partly) shadowing off the
LOS. While the shadowing variances envisioned for our deployment scenarios are
on the order of a few dB, we use a $10$ dB shadowing margin to provide high
link reliability. $F(d)$ is the mean pathloss, which depends on the distance
$d$ between transmitter and receiver

$F(d)=10\log_{10}\left\\{\lambda/(4\pi d)\right\\}^{n}$ (3)

where the pathloss coefficient $n$ is set to $2.5$ [14] and the wavelength
($\lambda$) is $5$ millimeter at $60$ GHz. $O(d)$ denotes the oxygen
attenuation, which can be computed as $O(d)=\frac{15}{1000}d$ when $d>200$ m.
Otherwise, it is ignored [14]. The noise power in dB, $P_{\text{noise,dB}}$ is
computed as:

$P_{\text{noise,dB}}=10\log_{10}\left(k_{B}T_{e}\cdot B\right)+F_{N}$ (4)

where $k_{B}T_{e}$ stands for the noise power spectral density ($-174$ dBm/Hz)
and $F_{N}$ is the noise figure of the receiver ($6$ dB). By combining the
above equations, approximately $200-300$ m is the maximum distance for
obtaining $1.5$ GBit/s data rate, i.e., successful uncompressed video
transmission.

###  III-B 60 GHz Outdoor Broadcasting Systems

As shown in Sec. III-A, the assistance of relays is required if the distance
between wireless cameras and a broadcasting center is more than $200-300$ m.
Furthermore, the size of a sports stadium (from wireless cameras to a
broadcasting center) is generally not more than $500$ m. Thus, we restrict the
number of relays to one. In our 60 GHz broadcasting system, three components
are existing, i.e., wireless video cameras, relays, and a broadcasting center.
As presented in Fig. 1(a), the proposed wireless video cameras have scalable
video coding (SVC) functionalities that reproduce the recorded video signals
as layered SVC-coded bitstreams. If the achievable rate of a 60 GHz link is
sufficient for uncompressed video streaming (i.e., more than $1.5$ Gbit/s),
all layers can be transmitted. Otherwise, the optimal coding level decision
module has to determine the number of layers. Each wireless video camera has
multiple-beam antennas. Therefore, each antenna can form $N$ independent
beams, so that the multiple streams created by SVC-encoding are divided into
$N$ parts and each part is assigned to a beam to be concurrently transmitted.
We furthermore assume that the relays have multiple-beam antennas for
reception, see Fig. 1(b). The relays aggregate the received signals and
transmit them towards a broadcasting center. As presented in Fig. 1(c), the
proposed broadcasting center has multiple antennas which are facing the
relays. We emphasize that due to the narrow beamwidth
($1.5^{\circ}$-$10^{\circ}$ [13]) of the antennas, multiple streams arriving
at the broadcasting center or relays do not interfere with each other.

###  III-C Objective

For this given system, our objective is the maximization of the delivered
total video quality. As shown in [4], the quality of video is related to the
data rate in a nonlinear fashion as a sublinearly, but monotonically,
increasing form. Following is one example of a quality function:

$f_{q}(a)=\frac{1}{\log_{\beta}(a_{\text{max}}+1)}\log_{\beta}(a+1)$ (5)

$\beta$ is a base ($1<\beta$), $a_{\text{max}}$ is a desired data rate for
uncompressed video streaming, and $a$ is a given data rate.

##  IV Mathematical Optimization Formulation

Fig. 2 shows the reference model with a set of sources $\mathcal{S}$, a set of
relays $\mathcal{R}$, and a single destination $D$. In the relay-destination
region (RDR) of Fig. 2, all relays are connected to $D$. Then the maximum
achievable rates of all relay and destination pairs can be computed (i.e.,
$a_{r_{1}\rightarrow D}^{\text{RDR}},\cdots,a_{r_{|\mathcal{R}|}\rightarrow
D}^{\text{RDR}}$). Our assumption is that $D$ can form a sufficient number of
independent beams so that it has no limitations concerning the number of
relays. Thus, we wish to find optimal combinations between sources and relays
in source-relay region (SRR) for the settings that both sources and relays can
form multiple beams. Then, our formulation for the maximization of delivered
total video quality is as follows:

$\max\sum_{j=1}^{|\mathcal{R}|}\sum_{i=1}^{|\mathcal{S}|}f_{q}\left(\frac{1}{2}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\right)x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ (6)

subject to

$\displaystyle\sum_{i=1}^{|\mathcal{S}|}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$
$\displaystyle\leq$ $\displaystyle\mathcal{A}_{r_{j}\rightarrow
D}^{\text{RDR}},\forall j,$ (7)
$\displaystyle\sum_{i=1}^{|\mathcal{S}|}x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}$ $\displaystyle\leq$ $\displaystyle B_{r_{j}},\forall j,$
(8) $\displaystyle\sum_{j=1}^{|\mathcal{R}|}x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}$ $\displaystyle\leq$ $\displaystyle B_{s_{i}},\forall i,$
(9) $\displaystyle\underline{a}_{s_{i}}$ $\displaystyle\leq$
$\displaystyle\sum_{j=1}^{|\mathcal{R}|}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}x_{s_{i}\rightarrow r_{j}}^{\text{SRR}},\forall i,$ (10)
$\displaystyle a_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ $\displaystyle\leq$
$\displaystyle\mathcal{A}_{s_{i}\rightarrow r_{j}}^{\text{SRR}},\forall
i,\forall j,$ (11) $\displaystyle x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$
$\displaystyle\in$ $\displaystyle\\{0,1\\},\forall i,\forall j,$ (12)

where $a_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ and $x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}$ stand for the Data rate between $s_{i}$ and $r_{j}$ and
Boolean connectivity index between $s_{i}$ and $r_{j}$, respectively. Note
that $s_{i}$ and $r_{j}$ stand for the source $i$, $\forall
i\in\\{1,\cdots,|\mathcal{S}|\\}$ and the relay $j$, $\forall
j\in\\{1,\cdots,|\mathcal{R}|\\}$, respectively. If $s_{i}$ and $r_{j}$ are
connected, $x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ is $1$ by (12).
Otherwise, $x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ is $0$ by (12). The
relays are all connected to $D$, thus, $x_{r_{j}\rightarrow
D}^{\text{RDR}}=1$. The $\mathcal{A}_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$
and $\mathcal{A}_{r_{j}\rightarrow D}^{\text{RDR}}$ are maximum achievable
rates computed by (1). In addition, the desired data rates between $s_{i}$ and
$r_{j}$ are less than $\mathcal{A}_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ as
shown in (11) where $f_{q}\left(\cdot\right)$ is a function for the
relationship between video quality and data rate (logarithmically and
monotonically increasing form). As shown in (10), the desired data rates
between $s_{i}$ and $r_{j}$ should exceed the defined minimum rates
($\underline{a}_{s_{i}}$, $\forall s_{i}$), which are minimum data rates for
guaranteeing the required minimum video qualities for each flow. Here,
$\mathcal{A}_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ from $s_{i}$ to $r_{j}$
and $\mathcal{A}_{r_{j}\rightarrow D}^{\text{RDR}}$ from $r_{j}$ to $D$ are
fixed. For each individual source, there are multiple outgoing flows (multiple
beams) toward relays, as formulated in (9) where $B_{s_{i}}$ stands for the
number of antenna-beams at source $i,\forall
i\in\\{1,\cdots,|\mathcal{S}|\\}$. Similarly, each relay can form multiple
beams in receiving mode, thus the number of incoming flows from sources can be
$B_{r_{j}}$ as formulated in (8) where it means the number of antenna-beams at
relay $j,\forall j\in\\{1,\cdots,|\mathcal{R}|\\}$. In (7), for each relay,
the summation of incoming rates from sources cannot exceed the data rate
between the relay and $D$. Finally, (6) describes the objective of finding the
pairs between sources and relays as well as finding the corresponding data
rates for maximizing the total video quality and the data rate value becomes
$1/2$ due to the half-duplex constraint.

######  Theorem 1.

The formulation in Section IV is non-convex.

###### Proof.

This proof considers the setting of one-source and one-relay. Then the
objective function becomes

$\displaystyle f\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}},x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}\right)$
$\displaystyle\triangleq$ $\displaystyle f_{q}\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\right)x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ (13)
$\displaystyle=$
$\displaystyle\mathcal{K}\log_{\beta}\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1\right)x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ (14)

where $\mathcal{K}=\frac{1}{\log_{\beta}(a_{\text{max}}+1)}$ is constant and
$x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}$ is relaxed, i.e., $0\leq
x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}\leq 1$. To show that this is non-
convex, the second-order Hessian of this should not be positive definite [15].
The Hessian $\nabla^{2}f\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}},x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}\right)$ is:

$\begin{bmatrix}0&\frac{\mathcal{K}/\ln\beta}{a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1}\\\ \frac{\mathcal{K}/\ln\beta}{a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1}&-x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\cdot\frac{\mathcal{K}/\ln\beta}{\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1\right)^{2}}\end{bmatrix}$ (15)

and then the corresponding two eigenvalues are

$\frac{\mathcal{I}}{2}\pm\frac{1}{2}\sqrt{\mathcal{I}^{2}+\left(\frac{2\mathcal{K}/\ln\beta}{a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1}\right)^{2}}$ (16)

where $\mathcal{I}=\frac{-\frac{\mathcal{K}}{\ln\beta}\cdot
x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}}{\left(a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}+1\right)^{2}}$, $0\leq a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\leq 1.5$, $0\leq x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\leq 1$.

These are not all positive, thus Hessian is not positive definite, which
proves the formulation is non-convex. ∎

For non-convex MINLP, heuristic searches can find approximate solutions but
cannot guarantee optimality [15]. With the following Theorem, our non-convex
MINLP can be re-formulated as a convex program form.

######  Theorem 2.

For the given formulation, (6)-(12), introducing

$a_{s_{i}\rightarrow r_{j}}^{\text{SRR}}\leq\mathcal{A}_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\cdot x_{s_{i}\rightarrow r_{j}}^{\text{SRR}},\forall
i,\forall j$ (17)

instead of (11) makes the formulation convex.

###### Proof.

For the non-convex MINLP formulation in Section IV, $x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}=0$ means the link is disconnected. Thus the corresponding
rate becomes $0$ and (17) leads to the same result when $x_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}=0$, i.e.,

$a_{s_{i}\rightarrow r_{j}}^{\text{SRR}}\leq\mathcal{A}_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\cdot 0=0,\forall i,\forall j.$ (18)

Otherwise, if $x_{s_{i}\rightarrow r_{j}}^{\text{SRR}}=1$, then this term is
equivalent to (11). Therefore, in turn, (6) is also updated as

$\max\sum_{j=1}^{|\mathcal{R}|}\sum_{i=1}^{|\mathcal{S}|}f_{q}\left(\frac{1}{2}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}\right)$ (19)

and (7) and (10) are also updated as following (20) and (21):

$\displaystyle\sum_{i=1}^{|\mathcal{S}|}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}}$ $\displaystyle\leq$
$\displaystyle\mathcal{A}_{r_{j}\rightarrow D}^{\text{RDR}},\forall j.$ (20)
$\displaystyle\underline{a}_{s_{i}}$ $\displaystyle\leq$
$\displaystyle\sum_{j=1}^{|\mathcal{R}|}a_{s_{i}\rightarrow
r_{j}}^{\text{SRR}},\forall i,$ (21)

Then now there are no non-convex terms in the program. ∎

Finally, our convex MINLP, which can guarantee optimal solutions, is as
follows: (19) subject to (20), (8), (9), (17), (21), (12) where $\forall
i\in\\{1,\cdots,|\mathcal{S}|\\},\forall j\in\\{1,\cdots,|\mathcal{R}|\\}$.

##  V Performance Evaluation

To verify the performance of our scheme, i.e., video quality maximization
(named as VQM), we compare it with the following schemes:

  * •

The joint video coding and relaying under the consideration of sum rate
maximization (named as SRM). In this case, the proposed objective function
(19) is:

$\max\sum_{j=1}^{|\mathcal{R}|}\sum_{i=1}^{|\mathcal{S}|}\frac{1}{2}a_{s_{i}\rightarrow
r_{j}}^{\text{SRC}}$ (22)

due to the fact that the quality is no longer considered.

  * •

The scheme in [10], which is an efficient algorithm that considers joint rate
selection and routing (named as JRSR) in terms of sum-rate maximization with
cooperative communication mode selection and no multiple-beams. For fair
comparisons, we adapt the scheme to our outdoor-stadium architecture (one-tier
relay) and allow only decode-and-forward relaying.

TABLE I: Expectation of Achieved Normalized Aggregated Video Quality | Multiple-Beams at $s_{i}$ and $r_{j}$
---|---
$|\mathcal{S}|$ | $|\mathcal{R}|$ | Setting | VQM | SRM | JRSR
5 | 10 | I | 4.166 | 3.873 | 3.331
5 | 10 | II | 4.934 | 4.647 | 4.165
5 | 10 | III | 4.681 | 4.397 | 3.632
10 | 5 | I | 4.164 | 3.871 | 3.352
10 | 5 | II | 4.954 | 4.620 | 4.182
10 | 5 | III | 4.664 | 4.371 | 3.650
10 | 10 | I | 8.813 | 8.451 | 5.483
10 | 10 | II | 9.817 | 9.452 | 6.336
10 | 10 | III | 9.312 | 8.958 | 5.795
10 | 15 | I | 8.883 | 8.574 | 5.633
10 | 15 | II | 9.872 | 9.563 | 6.456
10 | 15 | III | 9.383 | 9.074 | 5.927
15 | 10 | I | 13.420 | 11.765 | 6.483
15 | 10 | II | 14.902 | 13.255 | 7.376
15 | 10 | III | 14.403 | 12.755 | 6.839

For the setting, the cameras are uniformly distributed on top of the stadium.
Between stadium and broadcasting center, multiple relays are uniformly
deployed along a line. To vary the settings, we consider this line to be near
the cameras (Setting I), in the middle between cameras and broadcasting center
(Setting II), and near the center (Setting III). As our performance measure,
we consider the cumulative probability distribution (cdf) of the aggregate
video quality. The cdf is obtained as follows: we consider multiple
realizations of the deployment of sources and relays, i.e., the random
deployment of relays with Setting I, Setting II, and Setting III. For each
such realization, we optimize coding rates and relay selection; thus each run
gives us one realization of the aggregate video quality. We finally plot the
cdf of this quality. For the simulation of VQM, the lower bounds
($\underline{a}_{s_{i}}$, $\forall i\in\\{1,\cdots,|\mathcal{S}|\\}$) of each
source are set as $0.75$ Gbit/s ($50\%$ of $1.5$ Gbit/s). More detailed
scenarios and simulation results can be found in [4].

###  V-A CDF of Aggregate Video Quality

Figure 3: Impact of Various Lower Bound Setting:
$|\mathcal{S}|=10,|\mathcal{R}|=15$

(a) Setting I

(b) Setting II

(c) Setting III

Figure 4: Simulation Results: Number of Sources ($|\mathcal{S}|=5,10,15$) and
Fixed Number of Relays ($|\mathcal{R}|=10$)

Fig. 4 plots the cases that the number of sources is smaller, equal, or larger
than the number of relays (i.e., $|\mathcal{S}|=5,10,15$, and
$|\mathcal{R}|=10$). The mean achieved normalized aggregated video qualities
are in Table I. In Table I, the video quality values from each source are
normalized as $1$ for performance evaluation. Thus, if we have $N$ cameras in
the system, the maximum achievable aggregated video quality is $N$. As shown
in this result, the performance of JRSR is worse than that of both SRM and
VQM. The latter (i.e., JRSR), by design, does not allow the exploitation of
the multiple-beam antennas at relays, and thus shows worse performance. The
performance gains of SRM are more pronounced than JRSR in Settings I and III,
i.e., when the relays are close to either the sources or the destination. More
importantly, we find that the relative performance advantage drastically
increases as the number of sources increases relative to the number of relays.
This is not surprising, as for these situations the ability of the sources to
split their streams and flexibly route them via the relays becomes more
important. We also see that SRM shows lower performance than VQM due to the
fact that SRM aims to the maximization of sum data rates, while VQM aims to
maximize the overall delivered video quality. The relative advantage of VQM
also increases as the number of sources increases. Again, this is not
surprising, as the bandwidth limitations become more stringent as the number
of sources increases.

###  V-B Impact of Lower Bound Setting

In previous simulation, the lower bounds for the data rate per data stream are
set as $0.75$ Gbit/s. Here, we vary this value from $0$ Gbit/s (no lower
bound) to $1.5$ Gbit/s (allowing only uncompressed video) in steps of $0.1$
Gbit/s. As a performance quality measure, we define “stream outage” (i.e., the
probability that at least one stream does not have the minimum required
quality). As shown in Fig. 3, Setting III suffers significantly from the
higher required per-stream quality. With Setting III, the data rates between
sources and relays are lower than the others. Thus, when we set the lower
bound quite high, all flows are disconnected. Thus, it achieves the lowest
performance. On the other hand, in Setting I, all flows between sources and
relays have enough capacity to support uncompressed video transmission, thus,
a higher setting for minimum quality does not have a strong impact. Fig. 3
also shows that VQM has better performance than SRM for all settings.

##  VI Conclusion

This paper suggests and discusses quality-aware coding and routing for 60 GHz
multi-Gbit/s real-time video streaming in an outdoor broadcasting system. In
the system, there are multiple wireless video cameras distributed throughout
the stadium. We presented an optimization framework for finding the
combination of wireless link pairs between wireless cameras and relays that
can maximize the overall or per-flow qualities of delivered video to a
broadcasting center. An initial non-convex MINLP is re-formulated as a convex
program, which allows optimum solutions. Simulations show that this
methodology outperforms other methods that do not take the peculiarities of
millimeter-wave video links into account.

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