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Using subjective testing, we study the effect of the network parameters, delay and packet loss ratio, on the QoE of cloud gaming. We studied three different games, selected based on genre, popularity, content complexity and pace, and tested them in a controlled network environment, using a novel emulator to create realistic lognormal delay distributions instead of relying on a static mean delay, as used previously, we also used Parsec as a good representative of the state of the art. We captured user ratings on an ordinal Absolute Category Rating scale for three quality dimensions: Video QoE, Game-Playability QoE, and Overall QoE. We show that Mean Opinion Scores (MOS) for the game with the highest levels of content complexity and pace are most severely affected by network impairments. We also show that the QoE of interactive cloud applications rely more on the game playability than the video quality of the game. Unlike earlier studies, the differences in MOS are validated using the distributions of the underlying dimensions. A Wilcoxon Signed-Rank test showed that the distributions of Video QoE and Game Playability QoE are not significantly different.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase we get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation., Comment: 16 pages

The increasing popularity of the Video on Demand (VoD) applications like Netflix and YouTube has caused a large increase in video streaming over the network. To increase subscriptions and avoid customer churn, service providers are keen to evaluate the end-user experience more effectively. In this paper, we present an approach that can be used to evaluate the distribution of the Quality of Experience (QoE) metric evaluated from the measured Quality of Service (QoS) distributions. By using the network QoS measurements of delay and jitter, we find the probability density functions (PDF) for these parameters. Focussing on VoD applications employing improved VP9 codec (as used by Netflix) we use subjective testing and regression to map QoE onto jitter. The resulting QoE model and theory of direct propagation of probability density function was used to evaluate the distribution of QoE. We found that the empirical delay and jitter measurements follow a lognormal distribution whose characteristic parameters, sigma ( σ) and mu ( μ), vary for different network providers in the UK. Our results indicate that, even for slight variations of the underlying jitter distribution, the QoE distribution shows significant variations. These variations were recorded and presented as confidence intervals for four leading UK network providers.

In addition to the traditional QoS metrics of delay, delay jitter, and packet loss probability (PLP), Quality of Experience (QoE) is now widely accepted as a numerical proxy for actual user experience. The literature has reported many mathematical mappings between QoE and QoS. These QoS parameters are measured by the network providers using sampling. There are some papers studying sampling errors in QoS measurements; however there is no account of propagation of these sampling errors to QoE evaluation. In this paper, we used industrially acquired measurements of PLP and jitter to evaluate the sampling errors and correlation in measurements. Focussing on Video-on-demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLP and jitter. The resulting mathematical functions of QoE and theory of error propagation was used to evaluate the propagated error in QoE, and this error was represented as confidence interval. Using the guidelines of UK government for sampling, our results indicate that confidence intervals around estimated QoE in a busy hour can be between MOS=1 to MOS=5 at targeted operating point of QoS parameters. These results are a new perspective on QoE evaluation, and are of great significance to all organisations that need to estimate the QoE VoD applications precisely.

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.

The need for fresh water in the world of industry is increasing with the rapid development of the global industry. The shipping industry is having a significant impact as part of a global industry concerning the sector of freshwater demand on ships. Freshwater supplies on ro-pax vessels are very important because they are the source of crew and passenger life when ships sail. Fulfillment of freshwater needs on a ro-pax vessel is made in a conventional system by filling clean water into a freshwater tank from the port. In this final project will be analyzed technical and economical on designing fresh water system using reverse osmosis system with solar panel as power supply and compare it with conventional system on ship KM. SABUK NUSANTARA 56. This Final Project includes installation design, size of fresh water tank, amount of cargo, investment cost and operational cost. The conclusion obtained in this final project is a reverse osmosis (RO) system with solar panels as a very efficient power supply when applied to ro-pax ships such as ships KM SABUK NUSANTARA 56 because with this system the ship is able to produce their own fresh water, the size of the freshwater tank is smaller, for new shiploads can be increased. For the percentage of total cost of fresh water needs is dearer about 52 percent compared to conventional system and payload value increased 29.2 percent compared to conventional system.