Mobile Edge Computing : Analysis And Vision Of Mobile Edge Computing

In terms of architecture, it has been very challenging to provide platform, and ability to render service with high availability from the providers of the first generation (1G) networks to 4G

High-performance applications, for example, high-definition video, high-end CAD, and graphics applications, require enhancements to network bandwidth (Barford, 2008).

a hardware and software designed earlier. The hardware of 5G uses ultra wide band networks with higher bandwidth at low energy levels and the bandwidth is of 4000Mbps, which is about 400 times faster than existing generations. It uses the smart antenna system and CDMA for better performance. The software which is the core for functioning the device is arranged in such a way that the 5G will be single unified standard of different wireless networks including LAN technologies, LAN/WAN, wwww, unified IP and seamless combination of broadband. Software defines radio, encryption, flexibility, antivirus of the

There are 6 challenges of 5G mobile networks; higher capacity, higher data rate, lower E2E latency, massive device connectivity,

As 3G technologies are broader in use when compared to 4G are in limit [2]. The major difference between two is in the existence of yielding technologies. Many technologies EV-DO, HSPA and WCDMA these drop under 3G network. Whereas, many mobile phone companies are intelligent to dup their technologies as 4G; such as WiMax, LTE, and UMB. But, none of these technologies gives detailed description set forth by the 4G network standard, so they are often mentioned as Pre-4G or 3.9G. Here, are the some important points which are compared between two efficient technologies.

Advanced Quality of Service (QoS) inclusive of rate limiting based on the destination and source IP address.

The growth of the internet has had a positive effect on various aspects of human activities ranging from professional life to social life. More people are having access to the internet with time and the products and services offered through the internet are increasing. Online streaming is one of the services that have experienced a surge because of improved quality and access to the internet. Online video streaming from paid or free online channels are very popular among individuals, and they hold major entertainment promise. This can be justified by the varied nature of content that is streamed by internet users spread across the world. For instance, there is a wide range of the type of videos streamed

Due to the innovations in wireless technologies and recent developments in electronic circuits, there is ever increasing trend in the usage of mobile devices that are part of wireless networks. People of all lifestyles are using different kinds of mobile devices. In this context, there has been increased research in the multimedia applications that run over wireless networks.

Presently, File downloading based on automotive mode switch (AMS) algorithm, this approach is an effective but video content downloading does not always give good performance (3). When AMS method is used the internet buffering is less but the bandwidth is high and costly (3). Moreover AMS algorithm uses peers in order to provide the requested video by users (3), if the video is so long then it would take more peers in order to provide video, when there are more peers there is no proper scheduling of peers due to which when we want to get some unpopular video the peers provide a low quality video. By using our proposed method we are going to solve the problem of bandwidth and cost because agents provide their bandwidth resources constantly at a much lesser price.

(4G) Long Term Evolution (LTE) to provide higher data rates to end users by improving spectral efficiency, deploying more base stations, and/or aggregating more spectra. While some of the LTE enhancements, such as advanced multiple-input multiple-output (MIMO), Coordinated multipoint (CoMP), heterogeneous networks (HetNets), and carrier aggregation (CA), deliver the additional capacity needed to sustain the traffic surge for the next few years, none of them is seen as a viable solution to support the hundreds of times more traffic demands foreseen in 2020 and beyond, the so-called 5G era[5]. However, it is expected that in 5G millions more base stations (BSs) with higher functionality and billions more smart phones and devices with much higher data rates will be connected.[6]. In 5G mobile communication it is expected to have the presence of higher traffic volume, increased indoor or hotspot traffic, and higher energy [7]. The growth of wireless system capacity ever since the invention of the radio right up to the present can be attributed to three main factors: increase in the number of wireless infrastructure nodes, increased use of radio spectrum, and improvement in link efficiency. These three ingredients continue to be the dominant drivers of wireless capacity growth today [8]. As it is

Video streaming over LTE has seen a multiple times increase in its operation ever since it was launched. This was made possible due to the fact that LTE supports a high data rate. There are some difficulties to implement the LTE which will be discussed later. However in reality it is not the case. In this paper we would

over a 5G wireless network because of the low latency in the network so a huge transformation in the

In a Mobile Network Different kind of data flow over the network. In the recent year the communication over the Mobile Network in increasing very fast. Because of this the efficiency and the Quality of the service in Mobile Networks is affected. It results the delayed data transmission, Data Loss, Data Distortion and some other problems related to data. As the never size increased this problem becomes more critical. The proposed work is about to improve the QOS in a Mobile Adhoc Network. The proposed work is to improve the flow control respective to balancing the Bandwidth in Mobile Network. In this work we will work on Flow Control in Machanism in bottle neck flow control. Along with this we are providing an approach

The contribution of this paper is in two ways. We define three algorithms in the OTT VAN architecture which take application type or application quality into account. The objective is to address the application demands to achieve an effective network. We need evaluate these algorithms on a scenario basis. We will have to investigate a user watching a YouTube video clip under varying video quality, while both Cellular and Wi-Fi networks are available. We can evaluate the user aware quality, Cellular usage, and power consumption of the device using test-bed measurements. Cell phones get the energy from the batteries which is needed for their operation. Battery limit is seriously limited because of

4G allows IP-based voice, information and streaming multimedia at higher speeds and offers no less than 100 Mbit/s with high versatility and up to 1GBit/s with low portability (traveling). Various advancements thought to be 4G measures incorporate Long Term Evolution (LTE), Ultra Mobile Broadband (UMB) and the IEEE 802.16 (WiMax) standard. At the point when utilized as a part of MIMO frameworks it would have the capacity to give high information rate up to 100Mbps furthermore give QoS and Security administrations to end clients. The 4G goal is to meet difficulties displayed by the perpetually