50120140504012

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),

ISSN 0976 - 6375(Online), Volume 5, Issue 4, April (2014), pp. 105-118 © IAEME

105

SEAMLESS VIDEO STREAMING USING IMPROVED HANDOVER

PREDICTION AND SESSION HANDOVER IN MOBILE NETWORKS

Vidhate Amarsinh1, Devane Satish

2

1(Department of Computer Engg, RAIT, Nerul, Navi Mumbai)

2(Department of Information Technology, DMCE, Airoli, Navi Mumbai)

ABSTRACT

The application like video streaming on mobile devices has fetched a lot of attention in the

last decade. The significant problems like handover latency and lack of buffering are the real culprits

in the seamless continuity of video streaming applications targeted on mobile networks. This

paper presents a novel framework which considers an efficient handover prediction and

IntraDomain/InterDomain session handover as tools to take a charge of video continuity

under variable mobility conditions. The results of the simulation study shows that the proposed

framework can improve streaming continuity due to accurate handover prediction, proper

IntraDomain/InterDomain session handover with the support of session rate prediction.

Keywords: Video Streaming, Handover Decision, IntraDomain Handover, InterDomain Handover,

Session Rate.

1. INTRODUCTION

With the advancements of video streaming applications, an integration of mobility and

information services has become an urgent issue in the modern world. The video streaming

applications are span from traditional telecom services such as Voice over IP (VoIP) and video

conference to entertainment and Video on Demand (VoD) [1].

One of the most challenging issues in supporting mobility is the avoidance of flow

interruptions when clients roam from one wireless locality to another. The after effect triggers a

condition called video freeze. In order to provide uninterrupted services and maximum user-

perceived quality, a successful video streaming solution needs to adapt to mobile handover scenarios.

The handover decision typically considers only connectivity signal strength from various Access

Points (AP) in the proximity, which is not adequate to take handover decision for video streaming

applications.

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)

ISSN 0976 – 6375(Online)

Volume 5, Issue 4, April (2014), pp. 105-118

© IAEME: www.iaeme.com/ijcet.asp

Journal Impact Factor (2014): 8.5328 (Calculated by GISI)

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IJCET

© I A E M E



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The second major issue is Inter domain handover. An intelligent differentiation between Inter

Domain and Intra Domain session handover plays a vital role and reduces the probability of video

freeze. The Point of Attachments (PoA) like AP and Access Routers (AR) are equipped with an

inbuilt buffer. An Intelligent use of those buffers leads to save workload on the video servers.

There is also a need to predict the session rate before the handover takes place, as mobility

speed and connectivity strength varies. A weak framework without the consideration of above issues

typically hampers the video continuity [4][5].

Our work is motivated by 03 objectives. 1] Let the best AP must be selected based on the

various handover decision parameters other than just RSSI 2] A successful differentiation between

Intra Domain and Inter Domain to achieve an optimized workload saving. 3] A proactive session rate

prediction before handover, so as to calculate the exact frame after handover.

In this paper, we design and investigate a framework based on video streaming application to

take care of the above objectives. There is an extensive research has taken place in wireless network

handover decision and execution [2][3][6][7][11]. However the above issues in the context of video

streaming has not been addressed yet. In an attempt to give full scope to the topic, we have

intentionally suppressed several other issues related to handover like Layer 2 and Layer 3 handover

latencies, protocols etc.

2. LITERATURE SURVEY

Based on the motivation given above, we present an extensive literature survey to put

forward the core issues for video streaming continuity in mobile environment.

Franc Kozamernik [1] have reviewed the basic concepts of media streaming over the mobile

internet particularly those associated with Internet Protocols (IP), server technologies and delivery

aspects of video.

Khatib Noaman Ashraf, Vidhate Amarsinh and Satish Devane [2] have presented the state of

the art analysis for mobility management protocols along with a comparative study of signaling delay

and handover latency.

Vidhate Amarsinh & Devane Satish [3] have stated that the growth of audio, video and

multimedia applications are hampered because of limitation in MIPv6 during handover, as MIPv6 is

not designed for continuous streaming. They state the limitations to support QoS parameters like

variable jitter, delays in addition to loss of packets for streaming video during handover. They have

tried to improve the latency in handover by modifying the signals related to handover, which has

resulted in reducing the signaling cost and latency.

Xiaohuan Yan et al. [4] have presented a comprehensive survey of the handover algorithms

designed to satisfy various requirements based on parameters. To offer a systematic comparison,

they have categorized the algorithms into four groups based on the main handover decision criterion

used. Also, they have evaluated tradeoffs between their complexity of implementation and efficiency

for various proposals.

Pollini et. al. [5] has suggested various approaches to take Handover (HO) decision as RSS

with threshold, RSS with threshold and hysteresis and future prediction of RSS. Inclusion of

Threshold and Hysteresis Margin reduces Unnecessary HOs, but still a wrong decision for HO may

drop the call due to increase in HO delay. Especially hysteresis margin avoids ping-ponging effect.

Prediction of the future RSS helps in reducing unnecessary HO as compared to threshold and

hysteresis methods. But still RSS alone is not sufficient to take decision.

Ali Safa Sadiq et al. [6] states that traditional (based on one metric Received Signal Strength

Indicator) predictions of handover decisions do not perform well. It is a pressing need to develop an

intelligent approach to predict the handover decision process, thus yielding seamless handovers.

They have proposed a Mobility and Signal Strength-Aware Handover Decision (MSSHD) approach



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to predict the handover decision in wireless networks. The Received Signal Strength Indicator and

the direction of Mobile Node parameters are considered as inputs to the fuzzy inference system to

predict the handover decision, and hence switching to the best preferable access point, resulting in

reduced handover latency as well as the wireless access media delay.

Ravindra Agarwal and Amarsinh Vidhate [7] have stated that, many times the handover

decision is taken not only based on Received signal strength (RSS) but also other factors like

available bandwidth, total required bandwidth, expected delay, packet jitter, packet loss and cost per

byte. They have analyzed various handover decision techniques to understand the handover

initialization reasons and proper handover trigger.

P. Bellavista [8] et al. have considered that, with signal strength, other factors like handover

awareness, QoS awareness and location awareness are also some of the crucial factors to be

considered for handover decision. But more parameters introduce more delay, which may not be very

suitable for applications like video streaming.

Sanjay Dhar Roy et al. [9] have proposed received signal strength (RSS) based strategy for

handover in heterogeneous networks which considers RSS and bandwidth. Further these strategies

have been modified by considering averaging of RSS. For comparison purposes, the performance of

the VHO algorithm also considers hysteresis and dwell timer.

Wireless Bandwidth estimation tool (WBest) [10] was designed for fast, non-intrusive and

accurate estimation of available bandwidth in IEEE 802.11 networks.

Anagha Raich and Vidhate Amarsinh [11] have presented various parameters for the

selection of the best path based on signal strength, RTT and packet losses.

Amarsinh Vidhate and Satish Devane [12] have stated that the applications like video and

audio streaming does not sustain continuous data flow due to handover as it disconnects the flow

during handover over the mobile IPv6 networks. They have introduced a novel methodology on

session handover by using session rate prediction to enable video session continuity without video

freeze for mobile wireless networks. The results are presented to differentiate latency and workload

between IntraDomain and InterDomain session handover to facilitate seamless streaming over the

mobile networks.

Hua, K.A. et al. [13] have proposed a Dynamic Stream Merging (DSM) technique for

efficient video-on-demand services to mobile users on wireless mesh networks at the edges. DSM is

a new communication paradigm, in which multicast topologies are created incrementally through

dynamic merging of server streams at the mesh nodes. This is accomplished without the knowledge

of the server.

Sundaram, V. et al. [14] have proposed a light weight, fast and distributed scheme that uses a

session rate prediction technique and a Dynamic Relationship Tree (DRT) for wireless sensor

networks. The proposed session handover scheme promotes workload sharing and supports mobile

clients moving at speeds as high as 250 miles per hour.

Gunjgur, P.N. and Vidhate A.V. [15] have stated that the continuity of multimedia

applications may hamper due to improper session rates during transmission. In this study they have

surveyed various papers for session rate prediction of streaming media using network traffic

prediction methods. Their novel method as bandwidth estimation is carried out for the wireless

network which plays a significant role in predicting the session rates. Their proposed session rate

expression helps to understand the significance of predicting the session rate for streaming media in

mobile wireless network and studied various proposals in this regard alongwith the state-of-the-art

analytical analysis.

From the literature review, we list major issues like 1] There is an urgent need of a novel HO

prediction and decision algorithm 2] An appropriate differentiation between IntraDomain and

InterDomain for session continuity 3] A well prediction of a session rate. We present the framework

based on the above problems.



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3. VIDEO STREAMING FRAMEWORK

There is a prominent goal of better Quality of Experience (QoE) for the mobile user when the

video is being watched. A small freeze during the play keeps the user frustrated for whatever the

number of reasons. As an end user, he/she expects that the video should be played without interrupt

and with better quality of service. From the designer point of view, we divide the framework into 03

phases as

3.1. Proactive Handover prediction

3.2. IntraDomain / InterDomain Session Handover

3.3. Session Rate Prediction

3.1. Proactive Handover Prediction A handover decision plays a vital role for the continuance of the data flow during mobility

period. An intelligent selection of network parameters add to its right selection of PoA which leads

towards a valuable contribution for the applications like video streaming and its video continuity

during handover period. There are various network parameters identified which are listed below.

• Received Signal Strength Indicator (RSSI), Round Trip Time (RTT), Available Bandwidth,

Packet loss ratio, Peak Signal to Noise Ratio (PSNR), Network Connection Time (Lifetime),

Power Consumption, Monetary Cost, User preferences, Location and velocity of MN, Quality

of Service (QoS) and QoE etc.

• Other parameters: More coverage, lesser latency, lesser CIR (Carrier-to-Interferences Ratio),

lesser SIR (Signal-to-Interferences Ratio), lesser BER (Bit Error Rate), etc.), more security

level, proper QoS class based on the applications , are some of the parameters to be considered

for fulfilling the best selection of PoA.

Figure 1: Various parameters for handover decision

3.1.1. Handover Prediction In order to prevent the service interruptions while serving the video stream to MNs, the

prediction of handover is imperative. It allows performing the required service management

operations in advance with respect to the actual communication-level client handover. The main aim

of handover prediction depends on proactively moving the client to the exact predicted next wireless

location. This is done with the required buffered data for enabling the service continuity. In this

phase, the Handover prediction is performed proactively based on data link monitoring using the

Received Signal Strength Indication (RSSI) of clients. The handover decision is triggered when the

roaming client encounters a change in its link state.



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3.1.2. Proactive handover decision (Traditional) The proactive method of triggering the handover takes place in prior to the absolute loss of

original clients signal. i.e. if the RSSI of the new client crosses the threshold (Preset) value, then the

handover gets triggered [5].

We show the above proactive handover trigger using the following conditions.

Let RAP be the RSSI value of visible access point (AP)

Let RC be the RSSI value of current AP.

Let Thh be the threshold value of hysteresis handover.

If RAP > (RC + Thh)

Then

Handover is triggered

End if

The threshold value Thh helps in preventing heavy bouncing effects also called ping-pong

effect. If more predictions are simultaneously enabled, then the proactive handover considers AP

with strongest RSSI value, is considered.

3.1.3. (Improved) Proactive Handover Decision Algorithm

The sufficient indication of RSSI from the nearby APs, above the threshold gives a trigger to

handover. But it is not adequate to handle applications like video streaming due to the variable nature

of mobile networks in terms of bandwidth, delay, jitter and interference.

We consider four parameters for the AP selection function, viz RSSI, RTT, Packet Loss rate,

available bandwidth and PSNR.

In case of RSSI, it is to the fact that signal strength does not fade in a linear manner, but

inversely as the square of the distance. This means that if you are at a particular distance from an

access point and you measure the signal level, and then move twice as far away, the signal will

decrease by a factor of four. You move by 2x and the signal decreases by 1/4x; hence, the “inverse

square law”. The IEEE 802.11 standard defines a mechanism by which RF energy is to be measured

by the circuitry on a wireless NIC. This numeric value is an integer with an allowable range of 0-255

(a 1-byte value) called the Receive Signal Strength Indicator (RSSI).

RTT is the length of time it takes for a signal to be sent plus the length of time it takes for an

acknowledgment of that signal to be received. This time delay therefore consists of the propagation

times between the sender and the receiver.

PSNR is the ratio between the maximum possible power of a signal and the power of

corrupting noise that affects the fidelity of its representation. Because many signals have a very wide

dynamic range, PSNR is usually expressed in terms of the logarithmic decibel scale.

Packet Loss Ratio is the ratio between number of packets sent Vs number of packets received

and checked at different time intervals. The lesser the ratio, better is the performance. We restrict our

discussion to these four parameters only and consider for the decision making.

Available bandwidth is one of the important parameters as the target application is video

streaming, which is bandwidth consuming. Few statistics are presented here for the verifications. The

bandwidth for a video streamed at 300 kpbs watched by 50 clients for 01 hr. would be calculated as

300/8(conversion factor) =37.5, 37.5 X 60 (Seconds) = 2250, 2250 X 60 (Minutes) = 135000,

135000/ 1000 kb=135 mb/hr [16].

An Improved proactive method of triggering the handover takes place in prior to the absolute

loss of original clients signal. i.e. if the RSSI of the new client crosses the original value, and having

minimal RTT value , minimal packet loss and sufficient bandwidth available , then the handover gets

triggered. These parameters make sure about not only the next point of attachment but also the best

point of attachment for the range applications like audio/video streaming.



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We demonstrate the above proactive handover trigger function using the following

conditions. Let RAP be the RSSI value of visible Access Point (AP), RC be the RSSI value of current

AP, Thh be the threshold value of hysteresis handover, RTTmin is the minimum RTT value returned by

the RTTmin function, λmin is the minimum packet loss value returned by the λmin function.

The AP selection function is defined as

( , )ni F i

i

f W N= ∑ (1)

Initially the client requests the network and bandwidth availability information along its

projected route or probable route. The route is recognized in prior. The lookup service then replies

with the available network’s position, RTTs from various APs in the proximity, Received signal

strength of various APs and a sequence of bandwidth samples from the lookup server. This is

performed using crowd-sourcing technique [12]. In this method, the MN gathers the measurements

made at various geographical locations of the commutation route. By firing a query to the lookup

database server, the MN can able to get the information. This information is returned to the lookup

service server and stored in the look up data base for future use. Every AP is going to do so that the

information spreads to other APs.

New AP Selection algorithm

If RAP > (RC + Thh) & & RTTmin && λmin Then


min min max max min min_ * * ( * ) *available availablenew AP b b RTT RTT RSSI RSSI hhf W N W N W N T W Nλ λ= + + + +

(2)

Select the Best suitable AP/BS and start HO association/ Reassociation

End if

Else


_new AP c hhf R T= +

(3)

Select the suitable AP/BS and start HO association/ Reassociation

End

End.

Figure 2: New AP Selection Function

The normalized values are given as

m a x

'

m a x

kR S S I

R S S I R S S IN

R S S I

−=

m i n

m a x m i na v a i l a b l eb

b bN

b b

−=

− (4)

m i n

m i n

m a x m i n

R T T

R T T R T TN

R T T R T T

−=

− m i n

m i n

m a x m i n

N λ

λ λ

λ λ

−=

−



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Where the sum all weights is 1.

min max min1

avaulableb RTT RSSIW W W Wλ+ + + =

(5)

The common weights are considered as,

availablebW = 10 %,

minRTTW =10%,

maxRSSIW =70% & min

Wλ =10%

The parameters in table 1 include network ID, time of taking values, global positioning

system (GPS) coordinates, bandwidth, round-trip time and packet loss rate. This information is

adequate, if client mobility is random. This technique helps in selecting the available AP with the

most capacity for handover. These parameters are required to calculate strict session rate during

handover so that the quality of the video should not be reduced.

Table 1: Lookup Database

3.2. Intra Domain and Inter Domain Session Handover with session rate prediction.

Figure 3 demonstrates the intra and inter-domain handover. N1 and N2 represent the networks.

Mobile Node (MN) after identifying the foreign node performs inter or intra-domain handover based

on the node availability within or outside the network.

As discussed, a hierarchical tree is used to represent parent child relationship between various

hierarchies. Based on the topology, the buffering location will move from child to parent and vice

versa.

Figure 3: Intra-domain and Inter-domain Session Handover



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The advantages are multiple like, it will be able to multicast streaming tree based on parent-

child relationship, whenever there is a new request for a video stream. It will detect redundant

streams and merge using intelligent buffering, before sending them downstream. This way it can

save more bandwidth and relax the stress at the gateway in terms of signaling cost.

We use hierarchical tree concept for the parent child relationship between the nodes. The

objective behind this concept is that, to promote the workload sharing among the nodes and relax the

stress on the gateway further. This automatically reduces the signaling cost and promotes the

localized buffering.

There are various issues and challenges to provide seamless session handover when the

networks are wireless. The issues are as bandwidth and bandwidth estimation is variable, high packet

loss, dynamic adaptation, variable jitter and jitter delay, interference and error rate is high. Due to the

above issues, the QoS, QoE degrades and seamless session handover becomes a challenge.

There is a need of data localization at the nearby point of attachment so that session must be

continued without repeating the frames and reduce the load on the gateway or the server further. We

present the topology for video streaming in the below figure. We are targeting video applications like

video on demand where one video can be demanded by many clients simultaneously on the mobile

device.

Figure 4: Inter Domain and Intra Domain Handover

R1, R2 are the routers, AR1, AR2 and AR3 are the Access Routers (AR),BS stands for Base

stations and MN is a mobile node.

Video streaming requires many sessions and session comprises of many frames. To

seamlessly transfer video sessions, a typical session rate prediction is required. It’s a proactive

session rate prediction which calculates typical sessions post handover to enable session continuity.

Our attempt is to save sessions and frames transfer from gateway or video server every time, when

the demanded sessions are same and simultaneous. To enable a proper session rate prediction, a

bandwidth estimation ,frame size estimation and frame rate estimation are the vital parameters. In

wireless enviroments, these parameters are variable.



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3.2.1. IntraDomain Session Handover algorithm

Let FNi and HNi be foreign and home node respectively.

The steps involved in the intra domain handover are as follows

1) Each MN analyzes the FNi based on its Function fnew_AP. When it identifies a suitable FNi with the

help of fnew_AP, it initiates the handover process.

MN → REQHO _ HNi

The client sends a request message (HO_REQ) to HNi requesting handover with FNi.

HO_REQ includes the clients existing video frame sequence ID.

2) HNi upon receiving the request verifies its node cache which results in the following two

solutions.

a. If HNi contains the respective FNi in its cache, then it forwards HO_REQ to FNi. then FNi

acknowledges HNi by sending a reply message (HO_REP)

HNi → REQHO _ FNi

HNi ← REPHO _ FNi

b. If HNi does not contain FNi in its cache, then it forwards the request to parent node. This

continued until FNi receives HO_REQ

HNi → REQHO _ parent node

3) In prior to performing step 2 a), HNi computes the most probable video frame sequence using

session rate prediction technique (explained in subsection d) which has to be delivered subsequent

to handover process.

4) Now, FNi initiates early buffering using the computed sequence and upholds client activities after

connection.

Basically the above concept promotes two types of session handover, 1] InterDomain HO &

2] Intra Domain HO. In the figure 4, when the MN is moving from BS1 to BS2, it is termed as

IntraDomain HO and it promotes the buffering at the AR1 till it connects to BS2. But in case when

MN is connected to BS2 and now connecting to BS3 then it would be inter domain handover and the

contents has to be buffered at R2, instead of AR1 and AR2. We would like to shift the location of

buffer from child to parent, based on type of Handover.

In Intra Domain case, BS1 is the HN and BS2 is the FN, as they are from same domain, MN

sends HO_REQ to HN and as FN is in its cache, HN forwards the request to FN. Subsequently FN

sends HO-REP. Due to session rate prediction; the typical session after handover is predicted. And

the handover takes place. In case of redundant flows, the contents during handover are buffered at

AR1.



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3.2.2. Inter-Domain Session Handover Algorithm

The steps involved in inter-domain handover are as follows

1) Each MN analyzes the FNi based on its Function fnew_AP. When it identifies a suitable FNi with

maximum RSSI when compared to HNi, it initiates the handover process.

Client → REQHO _ HNi

The client sends a request message (HO_REQ) to HNi requesting handover with FNi.

HO_REQ includes the clients existing video frame sequence ID.

2) HNi upon receiving the HO_REQ broadcasts the message after parent node confirmation. i.e.

In order to prevent HNi from unnecessary broadcasts, the parent of HNi following the reception

of HO_REQ message wait until an update is received from its child for time t. Then it re-

broadcasts the message after time expiry.

3) Step 2 allows any FNi in different clients control also to detect request message.

4) FNi upon receiving the request responds with the reply message to HNi. Thus the handover is

similar to intra-domain handover.

In Inter-Domain Handover, MN is trying to connect from BS2 to BS3 due to its mobility.

Here BS2 is HN and BS3 is FN. Now MN sends HO-REQ to HN but it doesn’t have FN in its cache

as it lies to different domain. So child HN sends the request to parent AR1 and if it is not there also,

it forwards to its parent. Due to the child parent and its decision to place buffer for redundant

streams, it would save lot of workload on the server, as many contents are coming from the regional

buffers.

3.3. Session Rate Prediction

Handover is a typical challenge when the environment is variable and the application is like

video streaming. Sessions are formed by many frames and we need to estimate session rate, frame

rate and frame size to estimate session rate. It must happen proactively. We see the issues & the

challenges faced by mobile wireless network. Session Handover seem to be the significant

challenging problem over here. We need to estimate the fluctuating bandwidth [6].

In this phase, the home node computes the most probable sequence of video stream that needs

to be delivered for the MN during handover.

To enable a proper session rate prediction, a bandwidth estimation ,frame size estimation and

frame rate estimation are the vital parameters. In wireless enviroments, these parameters are variable.

Let Rv be the rate at which client receives the video streams.

Let RTTo be the optimal round trip time

Let Rseq be the sequence number of video stream segment from which the foreign node has to deliver

the MN to ensure seamless video streaming.

Rv = )(

)(

ic

ic tt

αα −

− (6)



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RTTo = ∑RTT*η (7)

Rseq = c

v

o

R

RTTα+ (8)

where ∑RTT = sum of the RTTs computed from each path along the route of request from the

home node to the foreign node

tc = current time , ti = the time when the client initiates handover

cα = current video sequence number at tc , iα = current video sequence number at ti

Eq (8) reveals that Rseq is dependent on RTTo. The selection of exact RTTo value is based on the

metricη .

/tT R T Tη ≈ ∑ (9)

Where Tt is the total time, the home node supports the client until re-association.

Tt in Eq (9) depends on transmission range ( txα ) and speed of the client (ν ).

i.e. If (ν = slow) || ( txα = large) Then

Tt increases

End if

Thus, η varies withν , txα and node count in the network.

4. SIMULATION RESULTS AND DISCUSSION

We have used widely known network simulator NS2 [17] for our simulation. For the

simulation purpose, we have used UDP Continuous Bit Rate (CBR) traffic.

Figure 5: crowd sourcing based lookup outcome (1st try and 2nd Try)

The screen shot in figure 5 show the result of crowd sourcing where data rate is 400 kpbs,

various RTTs from different APs spread across the cellular proximity, total bandwidth available and

the total packets lost are considered. Due to intelligent AP selection function, there is a tremendous

difference between the packet losses as shown in figure 3 and 4. There is a difference between the

bandwidth also. Due to an intelligent PoA selection, the packet losses are reduced from 1191 bytes to

101 bytes.



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Figure 6: Various RTT values Figure 7: RSSI and PSNR without and

with proposal

In figure 6, we want to show that minimal RTT as a parameter also plays important role in

best AP selection. In figure 7, our proposal allows the packets lost recovery, reducing the frame loss’

rate after the handover, so MN receives the frames that were not received during the connection

discontinuation. This increases the average PSNR of the video forwarded, monitored in the

transmission each 5 seconds.

We also present an empirical example to proof the work. The value of tc = 300 ms,

ti = 200ms, αc = 100, αi = 80, η = 0.2, ∑ RTT = 61.1, Rv = 5, RTTo = 12.22, Rseq = 102

The example shows that, post session handover continues at sequence packet number 102.

Few more values are shown in table 2.

Table 2 : Various results of sequence frames

Figure 8: Total time of association is variable

tc ti cα iα ∑RTT Rv RTTo Rseq

300 200 100 80 61.1 5 12.22 102

312 208 110 89 41.1 5 8.22 112

320 217 120 91 78.7 4 15.74 124

780 560 210 170 56.6 6 11.32 221



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Figure 8 is a typical representation of total time of association which is based on various

RTTs, which ultimately is based on velocity and transmission range of MN.

Figure 9: Handover Latency InterDomain / Figure 10: Saving of workload on

IntraDomain Session Handover Gateway/ Server

Figure 9 shows a lot of handover latency reduction when it comes to IntraDomain session

handover as well as it reduces lot of workload on the server/gateway, by detecting redundant frames

and buffering it for avoiding retransmission from the server. Figure 10 is a graph between latency in

seconds Vs no. of sessions handover. It is clearly observed that there is almost 80% saving of

handover latency between IntraDomain and InterDomain session handover, if it is detected

proactively. It also increases with number of session handovers if the video sessions are redundant.

This scheme is more suitable for the similar video which is being demanded by many users with a

small time difference.

5. CONCLUSION AND FUTURE RESEARCH

Handover plays a vital role for data forwarding with minor packet loss. After the experiments

being conducted in NS-2, we concluded that, the use of traditional mobility protocols face difficulties

during video session continuity due to longer handover latency and non buffering support during

connection discontinuity. Our novel framework not only selects the best PoA with a proactive

handover prediction, but also reduces latency and packet losses during connection discontinuity,

without hampering the video quality. An efficient session handover during mobility alongwith

IntraDomain / InterDomain differentiation enables video continuity and minimize video freeze also.

We have specifically suggested Intra Domain Session HO and Inter Domain Session HO, along with

session rate prediction which proves the better improvement and saving of workload on the server

that suit to the scalable video applications.

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