Video Streaming Quality of Experience (QoE): In-network Cache Prefetching and Moving QoE Models

Title from PDF of title page viewed June 21, 2021VitaIncludes bibliographical references (pages 97-106)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021Dissertation advisor: Deep MedhiVideo streaming accounts for a significant amount of traffic on the Internet. Users expect a high quality of experience with online video streaming. Service and content providers desire to provide a satisfying experience for end users. Therefore, developing metrics to measure users satisfaction of such services is crucial. Quality of Experience (QoE) of a user for a video streaming service is important for content providers. For video streaming, the DASH (Dynamic Adaptive Streaming over HTTP) standard is one of the common approaches for streaming used by content providers in which a video is divided into segments of different bit rates for delivery. In this research, we studied two inter-related problems for DASH video streaming: 1) in-network caching techniques, prefetching, and 2) QoE measurement and monitoring. In the first research direction, we focus on content providers utilizing in-network caching and prefetching in order to reduce video delivery latency to provide users a higher quality of experience and reduce the traffic load on the core network. The issue with current prefetching methods is that they do not utilize available resources well; thus, the end users are not able to receive the best possible QoE. These approaches are either mostly naive or they are not compatible with the DASH protocol and they are too complex consuming too much time and compute resources. We propose a smart video cache prefetching scheme for segment bitrates. Our prefetching approach is based on throughput values in the cache that are forecasted using previous throughput values from clients. Since in a cache environment, multiple clients contend for video segments in the cache, we assess the cache performance and also con- sider the impact on QoE for each client during contention. When comparing our scheme with an existing scheme, results show that our smart prefetching increases the cache hitrate and reduces the number of unused prefetches for the cache, thereby improving QoE of the clients. In our second research direction, we focus on objective QoE, for which a number of QoE models has been proposed. The limitations of the current models are that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. We refer to such models as static QoE models. In many situations, such as live events, ensemble QoE during the session is important to understand, especially for multiple clients together, for network and content providers. For this need, we propose two QoE models to capture QoE periodically during video streaming by multiple clients simultaneously, which we refer to as Moving QoE (MQoE) models. Our first model, MQoE_RF takes into consideration the nonlinear effect due to the bitrate gain and sensitivity from the bitrate switching frequency. Our second model, MQoE_SD focuses on capturing the standard deviation in the bitrate switching magnitude among segments. Then, we study the effectiveness of both the models in a multi-user mobile client environment. We compared our models with an extension of the Model Predictive Control (MPC) QoE model (referred to as MQoE_MO). Our study shows the robustness of our MQoE models. The results show how the MQoE models is able to more accurately capture the overall QoE behavior than the static QoE model and its extension.Introduction -- Literature survey -- Smart Cache Prefetching -- Smart Cache Prefetching evaluation -- Moving QoE Models -- Moving QoE Models evaluation -- Conclusio

Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to varying network conditions. However, due to rate adaptation in adaptive streaming, the quality of the videos at the client keeps varying with time depending on the end-to-end network conditions. Further, varying network conditions can lead to the video client running out of playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). It is important to quantify the perceptual QoE of the streaming video users and monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Towards this end, we present LSTM-QoE, a recurrent neural network based QoE prediction model using a Long Short-Term Memory (LSTM) network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time varying QoE. Based on an evaluation over several publicly available continuous QoE databases, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides superior performance across these databases. Further, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for QoE prediction

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

Video QoE Estimation using Network Measurement Data

More than even before, last-mile Internet Service Providers (ISPs) need to efficiently provision and manage their networks to meet the growing demand for Internet video (expected to be 82% of the global IP traffic in 2022). This network optimization requires ISPs to have an in-depth understanding of end-user video Quality of Experience (QoE). Understanding video QoE, however, is challenging for ISPs as they generally do not have access to applications at end user devices to observe key objective metrics impacting QoE. Instead, they have to rely on measurement of network traffic to estimate objective QoE metrics and use it for troubleshooting QoE issues. However, this can be challenging for HTTP-based Adaptive Streaming (HAS) video, the de facto standard for streaming over the Internet, because of the complex relationship between the network observable metrics and the video QoE metrics. This largely results from its robustness to short-term variations in the underlying network conditions due to the use of the video buffer and bitrate adaptation. In this thesis, we develop approaches that use network measurement to infer video QoE. In developing inference approaches, we provide a toolbox of techniques suitable for a diversity of streaming contexts as well as different types of network measurement data. We first develop two approaches for QoE estimation that model video sessions based on the network traffic dynamics of the HAS protocol under two different streaming contexts. Our first approach, MIMIC, estimates unencrypted video QoE using HTTP logs. We do a large-scale validation of MIMIC using ground truth QoE metrics from a popular video streaming service. We also deploy MIMIC in a real-world cellular network and demonstrate some preliminary use cases of QoE estimation for ISPs. Our second approach is called eMIMIC that estimates QoE metrics for encrypted video using packet-level traces. We evaluate eMIMIC using an automated experimental framework under realistic network conditions and show that it outperforms state-of-the-art QoE estimation approaches. Finally, we develop an approach to address the scalability challenges of QoE inference. We leverage machine learning to infer QoE from coarse-granular but light-weight network data in the form of Transport Layer Security (TLS) transactions. We analyze the scalability and accuracy trade-off in using such data for inference. Our evaluation shows that that the TLS transaction data can be used for detecting video performance issues with a reasonable accuracy and significantly lower computation overhead as compared to packet-level traces.Ph.D

Quality of experience driven control of interactive media stream parameters

In recent years, cloud computing has led to many new kinds of services. One of these popular services is cloud gaming, which provides the entire game experience to the users remotely from a server, but also other applications are provided in a similar manner. In this paper we focus on the option to render the application in the cloud, thereby delivering the graphical output of the application to the user as a video stream. In more general terms, an interactive media stream is set up over the network between the user's device and the cloud server. The main issue with this approach is situated at the network, that currently gives little guarantees on the quality of service in terms of parameters such as available bandwidth, latency or packet loss. However, for interactive media stream cases, the user is merely interested in the perceived quality, regardless of the underlaying network situation. In this paper, we present an adaptive control mechanism that optimizes the quality of experience for the use case of a race game, by trading off visual quality against frame rate in function of the available bandwidth. Practical experiments verify that QoE driven adaptation leads to improved user experience compared to systems solely taking network characteristics into account