Article

Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming

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Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is a popular over-the-top video content distribution technique that adapts the streaming session according to the user's network condition typically in terms of downlink bandwidth. This video quality adaptation can be achieved by scaling the frame quality, spatial resolution or frame rate. Despite the flexibility on the video quality scaling methods, each of these quality scaling dimensions has varying effects on the Quality of Experience (QoE) for end users. Furthermore, in video streaming, the changes in motion over time along with the scaling method employed have an influence on QoE, hence the need to carefully tailor scaling methods to suit streaming applications and content type. In this work, we investigate an intelligent DASH approach for the latest video coding standard H.265 and propose a heuristic QoE-aware cost-efficient adaptation scheme that does not switch unnecessarily to the highest quality level but rather stays temporarily at an intermediate quality level in certain streaming scenarios. Such an approach achieves a comparable and consistent level of quality under impaired network conditions as commonly found in Internet and mobile networks while reducing bandwidth requirements and quality switching overhead. The rationale is based on our empirical experiments, which show that an increase in bitrate does not necessarily mean noticeable improvement in QoE. Furthermore, our work demonstrates that the Signal-to-Noise Ratio (SNR) and the spatial resolution scalability types are the best fit for our proposed algorithm. Finally, we demonstrate an innovative interaction between quality scaling methods and the polarity of switching operations. The proposed QoE-aware scheme is implemented and empirical results show that it is able to reduce bandwidth requirements by up to 41% whilst achieving equivalent QoE compared with a representative DASH reference implementation.

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The widespread deployment and adoption of the Dynamic Adaptive Streaming over HTTP (DASH) standard is making Internet video-on-demand a 'standard' Internet application similar in impact as email and web browsing. While video streaming has been widely deployed and studied for decades, DASH-based streaming is very different as it involves adaptation both by the application and by TCP. The dynamics and implications of multiple levels of end-to-end congestion control are not well understood. The contribution of the research presented in this paper is twofold: first, we characterize the bandwidth consumption of a widely deployed DASH application (i.e., Netflix); second, we provide insight in how different implementations and different access networks can impact bandwidth consumption. Our results suggest that Netflix adaptation defaults to underlying TCP mechanisms during periods of heavy, sustained network congestion. However, the application algorithm is clearly intertwined with the underlying TCP mechanisms during periods of volatile network conditions. In one network scenario, we observed that a backlogged TCP flow achieved a throughput of 6 Mbps while a Netflix session (under similar path conditions) consumed less than 3 Mbps of bandwidth.
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The distribution of videos over the Internet is drastically transforming how media is consumed and monetized. Content providers, such as media outlets and video subscription services, would like to ensure that their videos do not fail, start up quickly, and play without interruptions. In return for their investment in video stream quality, content providers expect less viewer abandonment, more viewer engagement, and a greater fraction of repeat viewers, resulting in greater revenues. The key question for a content provider or a content delivery network (CDN) is whether and to what extent changes in video quality can cause changes in viewer behavior. Our work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies. To establish causality, we use Quasi-Experimental Designs, a novel technique adapted from the medical and social sciences. We study the impact of video stream quality on viewer behavior in a scientific data-driven manner by using extensive traces from Akamai's streaming network that include 23 million views from 6.7 million unique viewers. We show that viewers start to abandon a video if it takes more than 2 s to start up, with each incremental delay of 1 s resulting in a 5.8% increase in the abandonment rate. Furthermore, we show that a moderate amount of interruptions can decrease the average play time of a viewer by a significant amount. A viewer who experiences a rebuffer delay equal to 1% of the video duration plays 5% less of the video in comparison to a similar viewer who experienced no rebuffering. Finally, we show that a viewer who experienced failure is 2.32% less likely to revisit the same site within a week than a similar viewer who did not experience a failure.
Article
Adaptive (video) streaming over HTTP is gradually being adopted by content and network service providers, as it offers significant advantages in terms of both user-perceived quality and resource utilization. In this paper, we first focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming and Netflix) and one open-source player (Adobe's OSMF). We first examine how the previous three players react to persistent and short-term changes in the underlying network available bandwidth. Do they quickly converge to the maximum sustainable bitrate? We identify major differences between the three players and significant inefficiencies in each of them. We then propose a new adaptation algorithm, referred to as AdapTech Streaming, which aims to address the problems with the previous three players. In the second part of the paper, we consider the following two questions. First, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share that resource in a stable and fair manner? And second, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay, keeping the viewing experience “live”?
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In general, video quality adaptation and video quality evaluation are distinct activities. Most adaptive delivery mechanisms for streaming multimedia content do not explicitly consider user-perceived quality when making adaptation decisions. Equally, video quality evaluation techniques are not designed to evaluate instantaneous quality where the quality is changing over time. We propose that an Optimal Adaptation Trajectory (OAT) through the set of possible encoding exists, and that it indicates how to adapt encoding quality in response to changes in network conditions in order to maximize user-perceived quality. The subjective and objective tests carried out to find such trajectories for a number of different MPEG-4 video clips are described. Experimental subjective testing results are presented that demonstrate the dynamic nature of user perception with adapting multimedia. The results demonstrate that adaptation using the OAT out-performs conventional adaptation strategies in which only a single aspect of the video quality is adapted. In contrast, the OAT provides a mechanism to adapt multiple aspects of the video quality thereby giving better user-perceived quality in both the short and long term.
Article
Transmission control protocol (TCP) with its well-established congestion control mechanism is the prevailing transport layer protocol for non-real time data in current Internet Protocol (IP) networks. It would be desirable to transmit any type of multimedia data using TCP in order to take advantage of the extensive operational experience behind TCP in the Internet. However, some features of TCP including retransmissions and variations in throughput and delay, although not catastrophic for non-real time data, may result in inefficiencies for video streaming applications. In this paper, we propose an architecture which consists of an input buffer at the server side, coupled with the congestion control mechanism of TCP at the transport layer, for efficiently streaming stored video in the best-effort Internet. The proposed buffer management scheme selectively discards low priority frames from its head-end, which otherwise would jeopardize the successful playout of high priority frames. Moreover, the proposed discarding policy is adaptive to changes in the bandwidth available to the video stream.
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MPEG has recently finalized a new standard to enable dynamic and adaptive streaming of media over HTTP. This standard aims to address the interoperability needs between devices and servers of various vendors. There is broad industry support for this new standard, which offers the promise of transforming the media-streaming landscape.
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Experimental data are presented that clearly demonstrate the scope of application of peak signal-to-noise ratio (PSNR) as a video quality metric. It is shown that as long as the video content and the codec type are not changed, PSNR is a valid quality measure. However, when the content is changed, correlation between subjective quality and PSNR is highly reduced. Hence PSNR cannot be a reliable method for assessing the video quality across different video contents.
Article
In this paper we describe a subjective quality assessment experiment conducted to measure the impact of temporal artifacts on video quality and characterize the influence of content motion on perceived quality. We examined the human response to jerkiness and jitter by considering different levels of strength, duration and distribution of the temporal impairments. Using videos with high picture quality, we found that for intermediate and high frame rate values video quality was similar independently from the duration of the frame rate decimation. On the other hand, for very low frame rates, overall video quality decreased as the duration of the impairment increased. The results also show that a reduction of the temporal resolution over the entire video does not necessarily lead to a significant loss of quality. Finally, the results of this study do not confirm the traditional thinking of lower-motion content receiving a higher quality than high-motion content for a given frame rate decimation factor. Using several motion descriptors, we observed that for a given sub-optimum frame rate, perceived quality does not necessarily increase with decreasing motion magnitude. More particularly, we found that perceived quality of head-and-shoulders content is severely affected by frame rate decimation although it is characterized by very low motion. Our results suggest that motion magnitude is not the only factor affecting perception of temporal artifacts.
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