Tobias Hobfeld

A Survey on Quality of Experience of HTTP Adaptive Streaming

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

Challenges of QoE management for cloud applications

Cloud computing is currently gaining enormous momentum due to a number of promised benefits: ease of use in terms of deployment, administration, and maintenance, along with high scalability and flexibility to create new services. However, as more personal and business applications migrate to the cloud, service quality will become an important differentiator between providers. In particular, quality of experience as perceived by users has the potential to become the guiding paradigm for managing quality in the cloud. In this article, we discuss technical challenges emerging from shifting services to the cloud, as well as how this shift impacts QoE and QoE management. Thereby, a particular focus is on multimedia cloud applications. Together with a novel QoE-based classification scheme of cloud applications, these challenges drive the research agenda on QoE management for cloud applications.

SOS: The MOS is not enough!

When it comes to analysis and interpretation of the results of subjective QoE studies, one often witnesses a lack of attention to the diversity in subjective user ratings. In extreme cases, solely Mean Opinion Scores (MOS) are reported, causing the loss of important information on the user rating diversity. In this paper, we emphasize the importance of considering the Standard deviation of Opinion Scores (SOS) and analyze important characteristics of this measure. As a result, we formulate the SOS hypothesis which postulates a square relationship between the MOS and the SOS. We demonstrate the validity and applicability of the SOS hypothesis for a wide range of studies. The main benefit of the SOS hypothesis is that it allows for a compact, yet still comprehensive statistical summary of subjective user tests. Furthermore, it supports checking the reliability of test result data sets as well as their comparability across different QoE studies.

YouTube Can Do Better: Getting the Most Out of Video Adaptation

YouTube, as one of the major HTTP Adaptive Streaming video services, accounts for a large fraction of todays Internet traffic. Therefore, it is important to understand how efficiently YouTube uses available network resources. Previous work observed that the YouTube player replaces previously buffered segments with higher quality segments. This is good for the user as it increases the average quality level. However, the lower quality level segments are discarded and their traffic is redundant and therefore wasted. In this paper, we use two independent approaches to evaluate the efficiency of YouTubes quality adaptation algorithm. The first approach performs regression based on previously collected video views from a large experimental data set. In the second approach we formulate a mixed integer linear program and calculate the optimal video quality adaptation. The results show that the simplistic regression approach gives an accurate estimation of the optimal adaptation. Furthermore, the optimization shows that the Quality of Experience (QoE) can be significantly improved compared to the actual average quality level observed in the real-world experiments, demanding for better video quality adaptation mechanisms by YouTube.

Impact of Variances on the QoE in Video Streaming

In the current Internet video streaming is dominating fixed and mobile consumer traffic. However, wireless and mobile networks may lack bandwidth or suffer from severe bandwidth fluctuations. The question arises: What is the impact of bandwidth fluctuations on the Quality of Service (QoS) and the user perceived quality? The key contribution of this paper is the analysis of QoS in terms of application-level parameters (stalling duration and frequency) for reliable video-on-demand streaming. The QoS is mapped to Quality of Experience (QoE) based on an existing QoS-QoE model for HTTP streaming. Different network conditions, in particular average and variances of network bandwidths, and video bit rates are taken into account in the analysis. We approach this problem by modeling the video player as a queueing system and evaluate the transient phase of the non-stationary system by means of discrete-event simulations. The results of our study show that the characteristics of the network have a high impact on the QoS and a surprisingly low impact on the QoE under certain conditions.

Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS

Video streaming has established itself as the main method to consume multimedia content on the Internet. The most widespread technique is HTTP Adaptive Streaming which is used by large video service platforms such as YouTube and Netflix. In order to guarantee a high QoE, different adaptation strategies have been developed that lead to different video playout patterns. While some strategies may be more aggressive than others and often adapt the video quality, it is not clear which strategy is the best.,,In this paper, we want to identify the trade-off between the average video quality and switches in the quality during playout. We do this with a user-centric view and try to optimize the adaptation depending on the user preference with a quadratic program. This work allows to put existing and future video adaptation algorithms in perspective with respect to user preferences. Our results show that the video quality can already be increased greatly by allowing few switches while more switches lead to diminishing gains. This is a novel discovery that is important for user-centric QoE-management which is of high interest for ISPs and video service providers.

Performance model for waiting times in cloud file synchronization services

Over the last few years, the importance of cloud services for file synchronization has been increasing. With the release of network enabled cameras like Google Glass the trend of synchronizing new photos with the cloud has emerged as an important new use case for mobile networks. Multiple stakeholder are interested in optimizing this synchronization process according to different orthogonal metrics. The end user is interested in a fast synchronization of images as well as a low energy consumption of the client used for upload to the network. The network operator requires a low number of network connections per synchronization of a batch of images in order to reduce the signalling load of the mobile network. This paper answers the question if the goals of the stakeholders can be achieved by selecting an appropriate synchronization scheduling mechanism. Therefore, we first present a model for the waiting time of cloud file synchronization services. Second, we perform measurements on the popular Dropbox service to obtain parameters required in the model. Finally, we perform a parameter study over the considered mechanisms and parameters, suggest a preferable algorithm and identify trade-offs favorable for all stakeholders.