Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient QoE-aware scheme for video quality switching operations in dynamic adaptive streaming. Dynamic 1 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,
... 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 whilst 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. There has been a surge in the use of streaming technologies, multimedia services and applications in recent years. A recent forecast  has predicted that by 2019 the consumer Internet video will account for 80% of the total global traffic and long-form (more than 10 minutes of duration) video will be 72.9% of the Internet video traffic. A similar trend has been predicted for mobile networks such as the emerging Fifth-Generation mobile networks (5G). Considering the limitations in bandwidth and the error-prone nature of the Internet and mobile networks, the Dynamic Adaptive Streaming over HTTP (DASH) technique allows video streaming sessions to be adapted to the user's network condition typically in terms of downlink bandwidth through switching among predefined video segments of different bitrate levels. Video can be adapted by scaling different dimensions such as the frame quality, frame rate, frame resolution etc. to achieve different video bitrate levels in DASH. Moreover, different segment sizes have varying effects on users' Quality of Experience (QoE). Furthermore, the different quality scaling methods used in DASH introduce their own individual challenges such as visual artifacts that affect QoE. In this paper, we investigate optimization schemes for DASH-based adaptive video streaming, focusing on improving transmission efficiency (bandwidth saving) without compromising users' QoE as the design goal. We follow an empirical approach and conduct three different subjective evaluations including initial QoE measurements to motivate and inform the subsequent design, hypothesis and evaluation for a novel optimization algorithm, and further QoE validation of the algorithm. We also perform a fourth experiment to evaluate bandwidth savings using the proposed algorithm. A summary of these four experiments is shown in Table 1 . Based on these experiments, we evaluate how users perceive the interactions between different quality scaling techniques and various switching patterns/scenarios as the motion complexity within the video sequence changes over time. Our experimental findings show that an increase in bitrate level does not necessarily improve the QoE and we also demonstrate how the amplitude of switching influences the QoE. We then propose a heuristic QoE-aware cost-efficient framework that does not switch quickly to the highest bitrate under certain circumstances, but rather stays temporarily at an intermediate bitrate level to reduce bandwidth requirements whilst achieving a comparable QoE level with the highest bitrate level. It is worth highlighting that we employ the latest video codec standard H.265 (also known as High Efficiency Video Coding or HEVC)  and thus the obtained results are more relevant to the new-generation video streaming applications such as those envisioned for 5G mobile networks in line with the industry's vision  . The contributions of our proposed system are fivefold. First, the increased period of switching minimizes unnecessary switching, which causes flicker in very unstable network environments, thereby improving the QoE. Our proposed system isolates short-term throughput variations (as a result of TCP congestion control) from throughput congestions (as a result of persistent throughput variations). Secondly, the proposed scheme saves bandwidth while achieving a comparable level of quality with an intermediate bitrate, chosen temporarily as the optimal bitrate in fluctuating network conditions. Thirdly, our proposed scheme minimizes the amplitude of switching operations in fluctuating network conditions, which also improves the QoE. The fourth contribution is the heuristic reduction of inaccurate bandwidth estimations caused by short segment sizes, where short-term network capacity measurements lead to non-optimum quality adaptations. Finally, the achieved bandwidth savings also reduce power consumption with the consequent benefits for mobile devices, where battery resource is an ongoing issue coupled with processing capacity. The remainder of this paper is structured as follows. Section 2 provides an overview of adaptive video streaming and reviews the current state of the art. The initial empirical QoE experiments that have inspired our subsequent proposal are discussed in the section 3. Section 4 presents our novel adaptation system to achieve QoE-aware, cost-efficient DASH streaming, and analyzes the experimental results. Finally, the paper is concluded in section 5. Efficient QoE-Aware Scheme for Video Quality Switching Operations 22:3 ACM Trans. Multimedia Comput. Commun. Appl. XX, X, Article XX. Publication date: Month 2018.