A Brief Synthesis of QoS-QoE Methodologies Hala Rifa¨ ı, Samer Mohammed and Abdelhamid Mellouk LISSI, EA 3956 - University of Paris-Est Cr´ eteil 122 rue Paul Armangot, 94400 Vitry-Sur-Seine, FRANCE E-mails: {hala.rifai, samer.mohammed, mellouk}@u-pec.fr Abstract—This paper presents an overview of some existing techniques of Quality of Service QoS and Quality of Experience QoE. QoS stands for the quality of the network in terms of transporting data with a minimum of delay and packet loss and a maximum of bandwidth. The extremum values depend surely of the provided service. QoE defines the quality perceived by the user and is assessed at the terminal level. It can be computed through objective and subjective ways. A comparison of the two techniques as well as an improvement of the service by combining them are also addressed. I. I NTRODUCTION Evaluating a network amounts to the determination of the quality of the services transmitted through this network from end-to-end. The services englobe all kinds of data including the voice, image, video, etc. Some examples of detectable problems are the freezing of an image, the non-synchronization of voice and images in video transmissions, the loss of connec- tion, the delay in communication, etc. The most common way to achieve the quantification is by attributing a kind of rank for the transmission going from 1 to 5, 1 reflects a poor service while 5 reflects an excellent one. This way of measurement is the so-called Mean Opinion Score MOS. For many years, the assessment of a network has been per- formed objectively by measuring and/or computing different criteria that determine the quality of the network with respect to the transmitted data (delay, packet loss, bandwidth, etc.). This quantification is called the Quality of Service QoS of the network. However, this technique does not consider the user in the loop. Recently, a new methodology has been introduced to evaluate a network. It usually takes into consideration the opinion of the user concerning the afforded services. This new technique is called Quality of Experience QoE and is a subjective assessment of the transmission. One should also note that in the case of multimedia transmission, the evaluation of QoE is affected by the quality of the data at source, e.g. a multimedia file, for example, generated with a poor quality using a non-adaptable compression algorithm can deteriorate the evaluation. Recently, many studies have emerged aiming to ameliorate the QoS of a network based on the user’s perception or QoE within a closed loop. The paper is organized as follows. In section II, Quality of Service mechanisms are detailed. Quality of Experience methodologies are presented in section III. A comparison of the two techniques besides of improvements proposed in the literature are stated in section IV. Finally, some conclusions are given in section V. II. QUALITY OF SERVICE The quality of service QoS consists on defining a set of technologies called QoS mechanisms that allow the network manager to ensure an efficient optimization and/or control of the resources in order to afford the best services with the lowest cost. It can be determined by the capacity of a network to convey data in terms of availability, transmission rate, delay, packet loss, etc. and represents a kind of engagement of the services providers toward their clients. Different criteria contribute to evaluate a network QoS. One can cite the i) delivery guarantee, ii) protocol, iii) latency time, iv) jitter, v) congestion and vi) bandwidth. Before developing mechanisms allowing to obtain a good QoS of a network, let’s first present the traffic classification problem. A. Traffic classification It consists on discriminating traffic into different classes relative to the protocol used, port number, data type besides of some statistical information like packets size and arrival frequency. Examples of classes are: data, multimedia, e- mail, virus, etc. Each class has its own characteristics going from attributed priority to flow rate limit, shaping, routing, etc. These characteristics are, in general, determined by the network operator. The classification allows to treat classes differently in order to have a more appropriate management of the traffic along the network and consequently deliver a better QoS. For example, traffic classification can be achieved at the ingress point of a network in order to divide it into elementary flows, that are managed differently afterwards. Besides the previously mentioned approaches to achieve traffic classification, various techniques have been applied. A widely used technique consists of determining the traf- fic classes according to network flow parameters obtained from the packet headers then applying some statistical-based learning techniques to classify the traffic according to de- fined classes [1], [2]. Considering that a single level cannot unveil the generating application, a multilevel approach of addressing a traffic flow is developed in [3] and is not based on information based in the payload, therefore its accuracy is contested. A classification based on cosine similarity of packets is addressed in [4], hybrid technique in [5] consisting of determining, within the transport layer, sub-classes within two main classes discriminated as P2P (Peer-To-Peer) and non- P2P packets, etc. One should emphasize that most of these approaches are adaptable to encrypted applications. 978-1-4577-0908-1/11/$26.00 ©2011 IEEE