rewriting and paraphrasing

I have a rewriting and paraphrasing job. I am attaching one essay. You need to rewrite and paraphrase it differently so that it look different and not catch plagiarism but the essence and theme need to be same. You also need to check for proper grammar and sentence structures. You need to structure it in APA format with also formatting references in APA style properly.

The convergence of wireless networks and multimedia communications, linked to the swift development of services and the increasing competition, has caused user expectations of network quality to rise. Network quality has become one of the main targets for the network optimization and maintenance departments.

Traditionally, network measurements such as accessibility, maintainability, and quality were enough to evaluate the user experience of voice services [1]. However, for data services, the correlation between network measurements and user benefits is not as straightforward. Firstly, the data system, due to the use of packet switching, is affected by the performance of individual nodes and protocols through which information travels, and, secondly, radio resources are now shared among different applications. Under these conditions, the performance evaluation of data services is usually carried out by monitoring terminals on the real network.

The end-to-end quality experienced by an end user results from a combination of elements throughout the protocol stack and system components. Thus, the performance evaluation of the service requires a detailed performance analysis of the entire network (from the user equipment up to the application server or remote user equipment).

Quality of experience (QoE) is a subjective measurement of the quality experienced by a user when he uses a telecommunication service. The aim pursued when assessing the quality of service (QoS) may be the desire to optimize the operation of the network from a perspective purely based on objective parameters, or the more recent need of determining the quality that the user is actually achieving, as well as its satisfaction level. However, the QoE goes further and takes into account the satisfaction a user receives in terms of both content and use of applications. In this sense, the introduction of smartphones has been a quantitative leap in user QoE expectations.

Traditionally, QoE has been evaluated through subjective tests carried out on the users in order to assess their satisfaction degree with a mean opinion score (MOS) value. This type of approach is obviously quite expensive, as well as annoying to the user. Additionally, this method cannot be used for making decisions to improve the QoE on the move. That is why in recent years new methods have been proposed to estimate the QoE based on certain performance indicators associated with services. A possible solution to evaluate instantaneously the QoE is to integrate QoE analysers in the mobile terminal itself [2]. If mobile terminals are able to report the measurements to a central server, the

QoE assessment process is simplified significantly. Other solutions are focused on including new network elements (e.g., network analysers, deep packet inspectors, etc.) that are responsible for capturing the traffic from a certain service and analysing its performance [3]. For instance, the work presented in [4] investigates the problem of YouTube quality monitoring from an access provider’s perspective, concluding that it is possible to detect application-level stalling events by using network-level passive probing only. In other work, the evaluation of video-streaming quality in mobile terminals is addressed by monitoring objective parameters like packet loss rate or jitter [5].

However, whatever solution intended to estimate the QoE from traffic measurements requires some kind of mapping towards a QoE value. A possible solution to perform this process is to apply a utility function associated to the particular data service in order to map the application level quality of service (QoS) into QoE (in terms of MOS value). Many research works are focused in that direction. For instance, a generic formula that connects QoE and QoS parameters (for different packet data services) is proposed in [6]. The work presented in [7] addresses the perception principles and discusses their applicability towards fundamental relationships between waiting times and QoE for web services.

Other work quantifies the impact of initial delays on the user-perceived QoE for different application scenarios by means of subjective laboratory and crowd-sourcing studies

[8]. Subjective experiments drawing on the evaluation of objective and subjective QoE aspects by a user panel for quantifying QoE during mobile video are presented in [9,10].

Previous mentioned studies are mainly focused on QoS and/or QoE evaluation, but no action, procedure, or framework is proposed to enhance the end user quality. Only a few works tackle this issue; for instance, a QoE oriented scheduling algorithm is proposed in [11] to dynamically prioritize YouTube users against other users if a QoE degradation is imminent (based on the buffered playtime of the YouTube video player). Other research work provides a methodology for incorporating QoE into a network’s radio resource management (RRM) mechanism by exploiting network utility maximization theory [12]. In [13], a specification and testbed implementation of an application-based QoE controller are presented, proposing a solution for QoE control in next generation networks although they do not include any QoE modelling or estimation algorithm.

References

[1] G. Gomez and R. Sanchez, End-To-End Quality of Service Over

Cellular Networks: Data Services Performance Optimization in 2G/3G, John Wiley & Sons, 2005.

[2] A. Díaz, P. Merino, and F. J. Rivas, “Customer-centric measurements

on mobile phones,” in Proceedings on 12th IEEE International Symposium on Consumer Electronics (ICSE ’08), pp. 14–16, 2008. [3] J. K. Pathak, S. Krishnamurthy, and R. Govindarajan, “System, method, and apparatus for measuring application performance

management,” Patent number: WO03007115, 2003.

[4] R. Schatz, T. Hossfeld, and P. Casas, “Passive youtube QoE

monitoring for ISPs,” in Proceedings of the 6th International

Conference on Innovative Mobile and Internet Services in Ubiquitous

Computing (IMIS ’12), pp. 358–364, July

2012.

[5] I. Ketykó, K. De Moor, W. Joseph, L. Martens, and L. De Marez,

“Performing QoE-measurements in an actual 3G network,” in

Proceedings of the IEEE International Symposium on Broadband

Multimedia Systems and Broadcasting (BMSB ’10), pp. 1–6,

March 2010.

[6] M. Fiedler, T. Hossfeld, and P. Tran-Gia, “A generic quantitative

relationship between quality of experience and quality of

service,” IEEE Network, vol. 24, no. 2, pp. 36–41, 2010.

[7] S. Egger, T. Hossfeld, R. Schatz, and M. Fiedler, “Waiting times

in quality of experience for web based services,” in Proceedings

of the 4th International Workshop on Quality of Multimedia

Experience (QoMEX ’12), pp. 86–96, July 2012.

[8] T. Hossfeld, S. Egger, R. Schatz, M. Fiedler, K. Masuch, and

C. Lorentzen, “Initial delay vs. interruptions: between the devil

and the deep blue sea,” in Proceedings of the 4th International

Workshop on Quality of Multimedia Experience (QoMEX), pp.

1–6, July 2012.

[9] T. De Pessemier, K. De Moor, A. Juan, W. Joseph, L. De

Marez, and L. Martens, “Quantifying QoE of mobile video

consumption in a real-life setting drawing on objective and

subjective parameters,” in Proceedings of the IEEE International

Symposium on Broadband Multimedia Systems and Broadcasting

(BMSB ’11), pp. 1–6, June 2011.

[10] T. De Pessemier, K. De Moor, W. Joseph, L. De Marez, and

L. Martens, “Quantifying the in􀄘uence of rebuffering interruptions

on the user’s quality of experience during mobile video

watching,” IEEE Transactions on Broadcasting, no. 99, pp. 1–5,

2013.

[11] F. Wamser, D. Staehle, J. Prokopec, A. Maeder, and P. Tran-

Gia, “Utilizing buffered YouTube playtime for QoE-oriented

scheduling in OFDMA networks,” in Proceedings of the 24th

International Teletraffic Congress (ITC 24’ 12), pp. 1–8, September

2012.

[12] G. Aristomenopoulos, T. Kastrinogiannis, V. Kaldanis, G.

Karantonis, and S. Papavassiliou, “A novel framework for

dynamic utility-based QoE provisioning in wireless networks,”

in Proceedings of the IEEE Global Telecommunications Conference

(GLOBECOM ’10), pp. 1–6, 2010.

[13] J. Sterle, M. Volk, U. Sedlar, J. Bester, and A. Kos, “Applicationbased

NGN QoE controller,” IEEE Communications Magazine,

vol. 49, no. 1, pp. 92–101, 2011.