Performance Study of Adaptive Video Streaming in an Interference Scenario of FemtoMacro Cell Networks
Abstract
:1. Introduction
2. Related Work
3. System Model and Assumptions
4. A Hybrid Algorithm for Power Allocation in Macrocell/Femtocell Architecture
 Consider the case when a ($Slave{F}_{l}$) captures the $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ report generated by a $MU{E}_{i}$ where the $SIN{R}_{M,MU{E}_{i}}^{k}$ is fulfilling the Equations (4) and (5), respectively. In addition, when a $MU{E}_{i}$ is close to the neighboring ${F}_{l}$, it is considered that the channel gain ${G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}$ is known from a ${F}_{l}$; however, there is a lack of shadowing $S({d}_{Tx,Rx})$ and fast fading $F(\alpha )$ components, thus the $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ can be represented as follows:$$\phantom{\rule{2.em}{0ex}}RS{S}_{{F}_{l},MU{E}_{i}}^{k}=\frac{{P}_{{F}_{l}}^{k}}{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}},$$
 It is expected that an ${F}_{l}$ finds the $RS{S}_{M,MU{E}_{i}}^{k}$ or it can capture it directly from an $MU{E}_{i}$. Then, an ${F}_{l}$ minimizes the transmitted power ${P}_{{F}_{l}}^{k}$ in a certain context in order to ensure the required QoS of a victim $MU{E}_{i}$. To do so, the $RS{S}_{M,MU{E}_{i}}^{k}$ must be greater than the $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ as in Equation (9):$$RS{S}_{M,MU{E}_{i}}^{k}>RS{S}_{{F}_{l},MU{E}_{i}}^{k}.$$
 Then, ${F}_{l}$ can adjust the allocation from ${P}_{{F}_{l}}^{k}$ to ${P}_{{F}_{l}}^{{}^{\prime}k}$ according to Equation (9) based on $RS{S}_{M,MU{E}_{i}}^{k}$ and $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ and substituting them in Equation (9):$$\phantom{\rule{1.em}{0ex}}\frac{{P}_{{F}_{l}}^{k}}{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}}>\frac{{P}_{{F}_{l}}^{{}^{\prime}k}}{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}}.$$Equating the RSSs on each side implies that$${P}_{{F}_{l},MU{E}_{i}}^{{}^{\prime}k}=\frac{{P}_{{F}_{l}}^{k}.{{G}_{{F}_{l},MU{E}_{i}}^{k}}^{2}}{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}}.$$
Algorithm 1 Hybrid Power Allocation 
Initiate $SIN{R}^{Threshold}$ 
if $SIN{R}_{M,MU{E}_{i}}^{k}<SIN{R}^{Threshold}$ then 
calculate $RS{S}_{M,MU{E}_{i}}^{k}$ 
end if 
for all ${F}_{l}\in L$ do 
if $SIN{R}_{M,MU{E}_{i}}^{k}<SIN{R}^{Threshold}$ then 
calculate $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ 
end if 
if $RS{S}_{M,MU{E}_{i}}^{k}\le RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ then 
M identifies the received $RS{S}_{{F}_{l},MU{E}_{i}}^{k}$ on ${F}_{l}$ as the interfering signal 
end if 
end for 
for all $MU{E}_{i}\in I$ do 
find the average number of the MUEs impacted by femtocell 
$AV{G}_{(R{P}_{P{T}_{{F}_{l}}},{T}_{MUE})}=\frac{{\sum}_{{}_{MU{E}_{i}}}^{{}^{MU{E}_{X}}}R{P}_{P{T}_{{F}_{l}}}}{{T}_{MUE}}$ 
where 
$$R{P}_{P{T}_{{F}_{l}}}=\left\{\begin{array}{cc}1\hfill & if\phantom{\rule{0.222222em}{0ex}}P{T}_{{F}_{l}}\phantom{\rule{0.277778em}{0ex}}has\phantom{\rule{0.277778em}{0ex}}equal\phantom{\rule{0.222222em}{0ex}}\phantom{\rule{0.222222em}{0ex}}positions\phantom{\rule{0.222222em}{0ex}}to\phantom{\rule{0.277778em}{0ex}}{F}_{l}\hfill \\ 0\hfill & otherwise\hfill \end{array}\right.$$

end for 
for all ${F}_{l}\in L$ do 
sort $AV{G}_{(R{P}_{P{T}_{{F}_{l}}},{T}_{MUE})}$ in a descending order 
create Master and Slave clusters according to $P{T}_{{F}_{l}}$ 
calculate the maximum interference allowed by the victims $MU{E}_{i}$ 
$MI{}_{MU{E}_{i}}^{k}=\frac{RS{S}_{M,MU{E}_{i}}^{k}}{SIN{R}^{Threshold}}{N}_{0}$ 
for all ${F}_{l}\in $ MasterFs do 
calculate power adjustment 
${P}_{{F}_{l}}^{{}^{\prime}k}=\underset{{I}_{{F}_{l},MU{E}_{i}}^{k}}{\underbrace{MI{}_{MU{E}_{i}}^{k}.\frac{RS{S}_{{F}_{l},MU{E}_{i}}^{k}}{{\sum}_{{}_{l=1}}^{L}RS{S}_{{F}_{l},MU{E}_{i}}^{k}}}}.\underset{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}}{\underbrace{\frac{{P}_{{F}_{l}}^{k}}{RS{S}_{{F}_{l},MU{E}_{i}}^{k}}}}$ 
end for 
for all ${F}_{l}\in $ SlaveFs do 
calculate power adjustment 
${P}_{{F}_{l},MU{E}_{i}}^{{}^{\prime}k}=\frac{{P}_{{F}_{l}}^{k}.{{G}_{{F}_{l},MU{E}_{i}}^{k}}^{2}}{{G}_{{F}_{l},MU{E}_{i}}^{k}{}^{2}}$ 
end for 
end for 
5. Adaptive Resource Allocation for Video Streaming
5.1. Estimating Link Data Rate
 The achievable data rate ${u}_{m}$ for all video connections in the system is calculated according to their CQIs.
 The number of RBs for video connection in the system is calculated depending principally on the video and the physical layer data rates. Thus, the calculation of the number of RBs can be achieved by the following equation:$$h=\lceil \frac{{u}_{i}}{\frac{1}{{M}_{t}}{\sum}_{j\in {M}_{t}}{u}_{j}}\frac{\overline{{\mu}_{i}}}{\frac{1}{{M}_{t}}{\sum}_{j\in {M}_{t}}{\overline{\mu}}_{j}}\rceil ,$$
5.2. Matching between Resource Blocks and SVC Segments
6. Simulation Analysis
6.1. Network Topology
6.2. Performance Evaluation
7. Conclusions
Author Contributions
Conflicts of Interest
References
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Acronym  Meaning 

BL  Base Layer 
CMTDA  DistortionAware Concurrent Multipath Transfer 
CQI  Channel Quality Indicator 
CSG  Close Subscriber Group 
DASH  Dynamic Adaptive Streaming over HTTP 
DL  Downlink 
EL  Enhancement Layer 
ELBA  EnergyQuaLity Aware Bandwidth Aggregation 
EVIS  EnergyVideo Aware Multipath Transport Protocol 
FBS  Femtocell Base Station 
FDD  Frequency Division Duplex 
FUE  Femtocell User Equipment 
HAS  HTTP Adaptive Streaming 
HetNet  Heterogeneous Networks 
HTTP  Hypertext Transfer Protocol 
ILP  Integer Linear Programming 
LTE  LongTerm Evolution 
LTEA  LongTerm Evolution Advanced 
MBS  Macrocell Base Station 
MCS  Modulation and Coding Scheme 
MIMO  MultiInput Multioutput 
ML  Machine Learning 
MOS  Mean Opnion Score 
MUE  Macrocell User Equipment 
OFDMA  Orthogonal Frequency Division Multiple Access 
PA  Power Allocation 
PLC3DCS  Playback Length Changeable 3D Video Data Chunk Segmentation 
PLR  Packet Loss Ratio 
PSNR  Peak SignaltoNoise Ratio 
QoE  Quality of Experience 
QoS  Quality of Service 
RAN  Radio Access Network 
RB  Resource Block 
RSS  Received Signal Strength 
SDN  Software Defined Network 
SDQA  Streamlined DPbased Qualitylevel Allocator 
SINR  Signal to Interference Noise Ratio 
SVC  Scalable Video Coding 
TCP  Transmission Control Protocol 
TDD  Time Division Duplex 
UL  Uplink 
WLAN  Wireless Local Area Network 
WQUAD  Wireless Quality Adaptation 
Symbol  Meaning 

M  A macrocell 
$Fl$  A femtocell l 
L  Set of femtocells 
I  Set of MUEs 
J  Set of FUEs 
K  Set of RBs 
k  A RB k 
$MU{E}_{i}$  A macrocell user i 
$FU{E}_{j}$  A femtocell user j 
${N}_{0}$  Power of the additive White Gaussian noise 
$SIN{R}_{M,MU{E}_{i}}^{k}$  SINR of a given $MU{E}_{i}$ associated with MBS M on RB k 
${G}_{M,MU{E}_{i}}^{k}$  Channel fast fading gain between M and $MU{E}_{i}$ on RB k 
${G}_{{F}_{l},MU{E}_{i}}^{k}$  Channel fast fading gain between $MU{E}_{i}$ and the neighboring ${F}_{l}$ on RB k 
${Z}_{i,k}$  Set of all interfering FBSs on user $MU{E}_{i}$ on RB k 
${P}_{M,MU{E}_{i}}^{k}$  Transmit power allocated on RB k by the serving cell M 
$RS{S}_{M,MU{E}_{i}}^{k}$  Received signal strength by $MU{E}_{i}$ from MBS M on RB k 
$RS{S}_{{F}_{l},MU{E}_{i}}^{k}$  Received signal strength by $MU{E}_{i}$ from FBS ${F}_{l}$ on RB k 
${P}_{{F}_{l}}^{k}$  Transmit power allocated on RB k by the serving femtocell ${F}_{l}$ 
${P}_{{F}_{l}}^{{}^{\prime}k}$  New value of transmission power of femtocell ${F}_{l}$ on RB k 
$CL{S}_{y}$  Clusters 
y  Index of the cluster 
$P{T}_{{F}_{l}}$  Geographical position of the femtocell 
$R{P}_{P{T}_{{F}_{l}}}$  Repeated position 
${T}_{MUE}$  Total number of MUE 
${\overline{\mu}}_{i}$  Average traffic rate for video connection i 
Q  Operational mode set of the video encoder 
$L({d}_{T,R})$  Path loss exponent with the distance ${d}_{T,R}$ between T and R 
$S({d}_{T,R})$  Shadowing component 
$F(v)$  Fast fading component 
X  Total number of MUEs and FUEs 
${\overline{\mu}}_{c}$  Average traffic rate for video connection 
${u}_{k}$  Average data rate for the RB k 
X  Total number of MUEs and FUEs 
$RELs$  Rate of enhancement layer 
$RBL$  Rate of base layer 
Parameters  Values  Parameters  Values 

Macrocells  1  Thermal noise density  −174 dBm/Hz 
Femtocells  30  Carrier frequency  3.5 GHz 
MUEs  15–60  UE noise figure  2.5 dB 
FUEs  4  Macrocell radius  500 m 
bandwidth  10 MHz  FUEs average speed  3 km/h 
RB bandwidth  50  MUEs average speed  30 km/h 
Macro Tx power  43 dBm  $SIN{R}^{target}$  5.7 dB 
Femto Tx power  23 dBm  Simulation Time  60 s 
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Ali Yahiya, T.; Kirci, P. Performance Study of Adaptive Video Streaming in an Interference Scenario of FemtoMacro Cell Networks. Information 2018, 9, 22. https://doi.org/10.3390/info9010022
Ali Yahiya T, Kirci P. Performance Study of Adaptive Video Streaming in an Interference Scenario of FemtoMacro Cell Networks. Information. 2018; 9(1):22. https://doi.org/10.3390/info9010022
Chicago/Turabian StyleAli Yahiya, Tara, and Pinar Kirci. 2018. "Performance Study of Adaptive Video Streaming in an Interference Scenario of FemtoMacro Cell Networks" Information 9, no. 1: 22. https://doi.org/10.3390/info9010022