ترغب بنشر مسار تعليمي؟ اضغط هنا

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

71   0   0.0 ( 0 )
 نشر من قبل Alcardo Alex Barakabitze
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.



قيم البحث

اقرأ أيضاً

This paper proposes a novel QoE-aware SDN enabled NFV architecture for controlling and managing Future Multimedia Applications on 5G systems. The aim is to improve the QoE of the delivered multimedia services through the fulfilment of personalized Qo E application requirements. This novel approach provides some new features, functionalities, concepts and opportunities for overcoming the key QoE provisioning limitations in current 4G systems such as increased network management complexity and inability to adapt dynamically to changing application, network transmission or traffic or end-users demand.
Multimedia streaming to mobile devices is challenging for two reasons. First, the way content is delivered to a client must ensure that the user does not experience a long initial playback delay or a distorted playback in the middle of a streaming se ssion. Second, multimedia streaming applications are among the most energy hungry applications in smartphones. The energy consumption mostly depends on the delivery techniques and on the power management techniques of wireless access technologies (Wi-Fi, 3G, and 4G). In order to provide insights on what kind of streaming techniques exist, how they work on different mobile platforms, their efforts in providing smooth quality of experience, and their impact on energy consumption of mobile phones, we did a large set of active measurements with several smartphones having both Wi-Fi and cellular network access. Our analysis reveals five different techniques to deliver the content to the video players. The selection of a technique depends on the mobile platform, device, player, quality, and service. The results from our traffic and power measurements allow us to conclude that none of the identified techniques is optimal because they take none of the following facts into account: access technology used, user behavior, and user preferences concerning data waste. We point out the technique with optimal playback buffer configuration, which provides the most attractive trade-offs in particular situations.
Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station. Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the least information from content providers.
109 - David Baelde 2011
Generating multimedia streams, such as in a netradio, is a task which is complex and difficult to adapt to every users needs. We introduce a novel approach in order to achieve it, based on a dedicated high-level functional programming language, calle d Liquidsoap, for generating, manipulating and broadcasting multimedia streams. Unlike traditional approaches, which are based on configuration files or static graphical interfaces, it also allows the user to build complex and highly customized systems. This language is based on a model for streams and contains operators and constructions, which make it adapted to the generation of streams. The interpreter of the language also ensures many properties concerning the good execution of the stream generation.
Intelligent and autonomous troubleshooting is a crucial enabler for the current 5G and future 6G networks. In this work, we develop a flexible architecture for detecting anomalies in adaptive video streaming comprising three main components: i) A pat tern recognizer that learns a typical pattern for video quality from the client-side application traces of a specific reference video, ii) A predictor for mapping Radio Frequency (RF) performance indicators collected on the network-side using user-based traces to a video quality measure, iii) An anomaly detector for comparing the predicted video quality pattern with the typical pattern to identify anomalies. We use real network traces (i.e., on-device measurements) collected in different geographical locations and at various times of day to train our machine learning models. We perform extensive numerical analysis to demonstrate key parameters impacting correct video quality prediction and anomaly detection. In particular, we have shown that the video playback time is the most crucial parameter determining the video quality since buffering continues during the playback and resulting in better video quality further into the playback. However, we also reveal that RF performance indicators characterizing the quality of the cellular connectivity are required to correctly predict QoE in anomalous cases. Then, we have exhibited that the mean maximum F1-score of our method is 77%, verifying the efficacy of our models. Our architecture is flexible and autonomous, so one can apply it to -- and operate with -- other user applications as long as the relevant user-based traces are available.

الأسئلة المقترحة

التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا