Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is\nintroduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction\nbehavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social\nnetwork andmobile user behavior. Second, the influence between users is calculated according to the userâ??s influence, the interaction\nbehavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior,\nthe context information is considered; the context information and the order of user preferences are considered when calculating\nthe influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict\nmobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and\nthe integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those\nof existing methods....
Remote operation is a step toward the automation of mobile working machines. Safe and efficient teleremote operation requires\ngood-quality video feedback. Varying radio conditions make it desirable to adapt the video sending rate of cameras to make the best\nuse of the wireless capacity.The adaptation should be able to prioritize camera feeds in different directions depending on motion,\nongoing tasks, and safety concerns. Self-Clocked Rate Adaptation for Multimedia (SCReAM) provides a rate adaptation algorithm\nfor these needs. SCReAM can control the compression used for multiple video streams using differentiating priorities and thereby\nprovide sufficient congestion control to achieve both low latency and high video throughput.We present results from the testing\nof prioritized adaptation of four video streams with SCReAM over LTE and discuss how such adaptation can be useful for the\nteleremote operation of working machines....
With the surging demand on high-quality mobile video services and the unabated development of new network technology,\nincluding fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for\nvarious network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal\nfor investors and advertisers.Therefore, many works have focused on understanding how the factors, especially quality of service\n(QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled\nfrom QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well\nas feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first\npropose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our\nempirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model.\nThrough experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over\nthe baseline model which only considers QoS factors.The proposed model has potential for designing QoE-oriented scheduling\nstrategies in various network scenarios, especially in the fog computing context....
The Internet protocol television brought seamless potential that has revolutionized the media and telecommunication industries by\nproviding a platform for transmitting digitized television services. However, zapping delay is a critical factor that affects the quality of\nexperience in the Internet protocol television. This problem is intrinsically caused by command processing time, network delay, jitter,\nbuffer delay, and video decoding delay. The overarching objective of this paper is to use a hybrid delivery method that agglutinates\nmulticast- and unicast-enabled services over a converged network to minimize zapping delay to the bare minimum. The hybrid\nmethod will deliver Internet protocol television channels to subscribers using the unicast stream coupled with differentiated service\nquality of experience when zapping delay is greater than 0.43 s. This aids a faster transmission by sending a join message to the multicast\nstream at the service provider zone to acquire the requested channel. The hybrid method reported in this paper is bench marked with\nthe state-of-the-art multicast stream and unicast stream methods. Results show that the hybrid method has an excellent performance\nby lowering point-to-point queuing delay, end-to-end packet delay, and packet variation and increasing throughput rate....
In the last years, the video content consumed by mobile users has increased exponentially. Since mobile network capacity cannot\nbe increased as fast as required, it is crucial to develop intelligent schedulers that allocate radio resources very efficiently and are\nable to provide a high Quality of Experience (QoE) to most of the users. This paper proposes a new and effective scheduling\nsolutionâ??theMaximum Buffer Filling (MBF) algorithmâ??which aims to increase the number of satisfied users in video streaming\nservices provided by wireless networks. The MBF algorithm uses the current buffer level at the client side and the radio channel\nconditions, which are reported to the network by the client, as well as the bitrate of the requested video segment. The proposed\nscheduling strategy can also fulfill different satisfaction criteria, since it can be tuned to maximize the numbers of users with high\nQoE levels or to minimize the number of users with low QoE levels. A simulation framework was developed, considering a Long\nTerm Evolution (LTE) scenario, in order to assess the performance of the proposed scheduling scheme and to compare it with other\nwell-known scheduling solutions.The results show the superior performance achieved by the proposed technique, in terms of the\nnumber of satisfied and unsatisfied users....
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