Measuring and Improving the Quality of Experience of Adaptive Rate Video

Today's popular over-the-top (OTT) video streaming services such as YouTube, Netflix and Hulu deliver video contents to viewers using adaptive bitrate (ABR) technologies. In ABR streaming, a video player running on a viewer's device adaptively changes bitrates to match given network conditions. However, providing reliable streaming is challenging. First, an ABR player may select an inappropriate bitrate during playback due to the lack of direct knowledge of access networks, frequent user mobility and rapidly changing channel conditions. Second, OTT content is delivered to viewers without any cooperation with Internet service providers (ISPs). Last, there are no appropriate tools that evaluate the performance of ABR streaming along with video quality of experience (QoE). This thesis describes how to improve the video QoE of OTT video streaming services using ABR technologies. Our analysis starts from understanding ABR heuristics. How does ABR streaming work? What factors does an ABR player consider when switching bitrates during a download? Then, we propose our solutions to improve existing ABR streaming from the perspective of network operators who deliver video content through their networks and video service providers who build ABR players running on viewers' devices. From the network operators' point of view, we propose to find a better video content server based on round trip times (RTTs) between an edge node of a wireless network and available video content servers when a viewer requests a video. The edge node can be an Internet Service Provider (ISP) router in a Wi-Fi network and a packet data network gateway (P-GW) in a 4G network. During the experiments, our solution showed better TCP performance (e.g., higher TCP throughput during playback) 146 times out of 200 experiments (73%) over Wi-Fi networks and 162 times out of 200 experiments (81%) over 3G networks. In addition, we claim that the wireless edge nodes can assist an ABR video player in selecting the best available bitrate by controlling the available bandwidth in the radio access network between a base station and a viewer's device. In our Wi-Fi testbed, the proposed solution saved up to 21% of radio bandwidth on mobile devices and enhanced the viewing experience by reducing rebufferings during playback. Last, we assert that software-defined networking (SDN) can improve video QoE by dynamically controlling routing paths of video streaming flows based on the provisioned networking information collected from SDN-enabled networking devices. Using an off-the-shelf SDN platform, we showed that our proposed solution can reduce rebufferings by 50% and provide higher bitrates during a download. From the perspective of video service providers, higher video QoE can be achieved by improving ABR heuristics implemented in an ABR player. To support this idea, we investigated the role of playout buffer size in ABR streaming and its impact on video QoE. Through our video QoE survey, we proved that a large buffer does not always outperform a small buffer, especially under rapidly varying network conditions. Based on this finding, we suggest to dynamically change the maximum buffer size in an ABR player depending on the current capacity of its playout buffer for improving the QoE of viewers. During the experiments, our proposed solution improved the viewing experience by offering 15% higher average played bitrate, 70% fewer bitrate changes and 50% shorter rebuffering duration. Our experimental results show that even small changes of ABR heuristics and new features of network systems can greatly affect video QoE. However, it is still difficult for video service providers or network operators to evaluate new ABR heuristics or network system changes due to lack of accurate QoE monitoring systems. In order to solve this issue, we have developed YouSlow ("YouTube Too Slow!? - YouSlow") as a new approach to monitoring video QoE for the analysis of ABR performance. The lightweight web browser plug-in and mobile application are designed to monitor various playback events (e.g., rebuffering duration and frequency of bitrate changes) directly from within ABR video players and calculate statistics along with video QoE. Using YouSlow, we investigate the impact of the above playback events on video abandonment: about 10% of viewers abandoned the YouTube videos when the pre-roll ads lasted for 15 seconds. Even increasing the bitrate can annoy viewers; they prefer a high starting bitrate with no bitrate changes during playback. Our regression analysis shows that bitrate changes do not affect video abandonment significantly and the abandonment rate can be estimated accurately using the rebuffering ratio and the number of rebufferings. The thesis includes four main contributions. First, we investigate today's popular OTT video streaming services (e.g., YouTube and Netflix) that use ABR streaming technologies. Second, we propose to build QoS and QoE aware video streaming that can be implemented in existing wireless networks (e.g., Wi-Fi, 3G and 4G) and in SDN-enabled networks. Third, we propose to improve current ABR heuristics by dynamically changing the playout buffer size under varying network conditions. Last, we designed and implemented a new monitoring system for measuring video QoE

In-network video QoE estimation method exploiting error characteristics of OpenFlow statistics

これまでにアプリケーションのQoEを指標とした無線ネットワーク資源の有効利用に取り組んでいる. QoEを用いることにより複数の異なるアプリケーションが同一ネットワーク上で共存した場合でも, それぞれの品質を同じ基準で評価できるため, 一部のアプリケーションによる資源占有を避けて効率的な利用が可能となる. 本研究は, 転送アプリケーションフローのQoEを推定するために, ネットワーク内で得られるOpenFlowの統計情報を用いる. 先行研究では, パケットロス率が動画通信のQoEに大きく影響を与えることを示し, パケットロス率の測定誤差を補正するQoE推定手法を提案した. 一方, パケットロス環境では測定誤差の補正が十分でなく, QoE推定精度は劣化した. そこで本研究では, OpenFlowの誤差特性に基づいたパケットロス率の測定誤差を補正する新しいQoE推定手法を提案する. 実機実験を通じて提案手法がパケットロス環境においてQoEを正しく推定できる事を示した. / Toward efficient resource utilization, we have been tackling a SDN-enabled flow control considering application QoE. QoE helps us to handle application flows with the same basis even multiple different applications coexist on network. In this study, we estimate QoE of video streaming application based on the statistical information obtained from OpenFlow. The previous study showed that the packet loss rate is a key factor to estimate QoE on video streaming application, and proposed QoE estimation method which corrected measurement errors of OpenFlow. On the other hand, since the estimation accuracy of the previous method becomes worse in case of packet losses happening, in this study, we propose an improved QoE estimation method for video streaming that can distinguish between measurement errors and packet losses by exploiting the trend of occurrence in measurement errors. Through experiments, we showed that our proposed method can provide an QoE estimation even under the packet loss environments.電子情報通信学会 ネットワークシステム研究会（NS）, 2020年1月23日-24日, 沖縄県石垣市, 日

Delivery of adaptive bit rate video: Balancing fairness, efficiency and quality

HTTP streaming currently dominates Internet traffic. It is increasingly common that video players employ adaptive bitrate (ABR) streaming strategies to maximise the user experience by selecting the highest video representation while targeting stall-free playback. Our interest lies in the common situation where a set of video flows are competing for access to a shared bottleneck link, such as in a cellular radio access network. We observe that ISPs (e.g. cellular operators) are considering innetwork techniques for resource allocation and sharing among different users. Buoyed by the ability of software defined networks (SDN) to offer flow-specific control and traffic shaping, we focus on traffic shaping techniques, and experimentally analyse the effect on ABR video flows when sharing a bottleneck link. We conduct experiments using the GPAC video player operating over a Mininet virtual network. We conclude that traffic shaping can allow a balance of fairness, efficiency and quality. Traffic shaping ABR videos reduce the number of stalls and quality switches, while also reducing the peaks for the aggregate network traffic

An SDN-approach for QoE management of multimedia services using resource allocation

Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values

Performance evaluation of caching placement algorithms in named data network for video on demand service

The purpose of this study is to evaluate the performance of caching placement algorithms (LCD, LCE, Prob, Pprob, Cross, Centrality, and Rand) in Named Data Network (NDN) for Video on Demand (VoD). This study aims to increment the service quality and to decrement the time of download. There are two stages of activities resulted in the outcome of the study: The first is to determine the causes of delay performance in NDN cache algorithms used in VoD workload. The second activity is the evaluation of the seven cache placement algorithms on the cloud of video content in terms of the key performance metrics: delay time, average cache hit ratio, total reduction in the network footprint, and reduction in load. The NS3 simulations and the Internet2 topology were used to evaluate and analyze the findings of each algorithm, and to compare the results based on cache sizes: 1GB, 10GB, 100GB, and 1TB. This study proves that the different user requests of online videos would lead to delay in network performance. In addition to that the delay also caused by the high increment of video requests. Also, the outcomes led to conclude that the increase in cache capacity leads to make the placement algorithms have a significant increase in the average cache hit ratio, a reduction in server load, and the total reduction in network footprint, which resulted in obtaining a minimized delay time. In addition to that, a conclusion was made that Centrality is the worst cache placement algorithm based on the results obtained

SDN-based time-domain error correction for in-network video QoE estimation in wireless networks

Our previous study proposed a channel utilization method in Software-Defined Networking (SDN) enabled multi-channel wireless mesh network (SD-WMN), which utilizes all of channel resources efficiently. However, when different types of applications are transferred together, their QoE cannot be maintained because of differences in important factors affecting QoE among these applications. Therefore, in order to handle application flows more efficiently based on QoE, this paper focuses on QoE estimation for every ongoing flows through SD-WMN. Since some parameters required for QoE calculation cannot be obtained from OpenFlow, we estimate QoE based on not only the results from SDN-based measurement but also the estimated values of parameters. Finally, we showed that our proposed method is effective for video QoE estimation, especially in a case where there is no packet loss.11th International Conference on Intelligent Networking and Collaborative Systems(INCoS 2019), September 5-7, 2019, Oita, Japa

Adapting reinforcement learning for multimedia transmission on SDN

[EN] Multimedia transmissions require a high quantity of resources to ensure their quality. In the last years, some technologies that provide a better resource management have appeared. Software defined networks (SDNs) are presented as a solution to improve this management. Furthermore, combining SDN with artificial intelligence (AI) techniques, networks are able to provide a higher performance using the same resources. In this paper, a redefinition of reinforcement learning is proposed. This model is focused on multimedia transmission in a SDN environment. Moreover, the architecture needed and the algorithm of the reinforcement learning are described. Using the Openflow protocol, several sample actions are defined in the system. Results show that using the system users perceive an increase in the image quality three times better. Moreover, the loss rate is reduced more than half the value of losses recorded when the algorithm is not applied. Regarding bandwidth, the maximum throughput increases from 987.16 kbps to 24.73 Mbps while the average bandwidth improves from 412.42 kbps to 7.83 Mbps.Ayudas para contratos predoctorales de Formación del Profesorado Universitario FPU (Convocatoria 2015), Grant/Award Number: FPU15/06837; Programa Estatal de Investigación Científica y Técnica de Excelencia (Convocatoria 2017), Grant/Award Number: TIN2017-84802-C2-1-P; Programa Estatal De Investigación, Desarrollo e Innovación Orientada a los retos de la sociedad (Convocatoria 2016), Grant/Award Number: TEC2016-76795-C6-4-R; ERANETMED, Grant/Award Number: ERANETMED3-227 SMARTWATIRRego Mañez, A.; Sendra, S.; García-García, L.; Lloret, J. (2019). Adapting reinforcement learning for multimedia transmission on SDN. Transactions on Emerging Telecommunications Technologies. 30(9):1-15. https://doi.org/10.1002/ett.3643S11530