A Mobile Fog Computing‐Assisted DASH QoE Prediction Scheme

Abstract

Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

“…Cost efficiency fairness, video resolution, guided bitrates, video chunk quality, switching frequency, number of stalls, startup delay, average video quality Yes High/Medium [73], [250]- [252] QoE-aware Cross-layer Optimization buffer level, video playback continuity, fairness-index and stability index [260]; utility-QoE and system throughput for software-defined vehicle networks [251]; acceptance ratio, revenue-to-cost ratio and bandwidth [252] No High/Medium [192], [260]- [264] Application-level Optimization video playback bitrate, segment duration, average download bitrate and buffer level [260], [263]; average video quality, buffer starvation/filling and initial startup delay, streaming encoding rate [261], [262] No High/Medium [157], [232], [233], [235], [237], [226], [265] Transport Level Optimization using TCP/MPTCP PSNR, e2e delay, and goodput [226], [237]; quality switches, startup delay [265] No High/Medium [121], [266], [267], [268], [10], [269], [60], [270], [35] [271]- [283] QoE-Optimization using other Emerging Network Architectures Delay, backhaul traffic load, hit ratio of cache resources [267]; average throughput, RRT latency [276], [268], buffer size [284]; user density [10]; switching frequency, initial buffer delay, average video...…”

“…QoE-aware Adaptive Streaming over Cloud/Fog computing QoE-based resource management [274], [276], [277], [304]- [306], QoE optimization with energy efficiency [285] Perform QoS/QoE-aware orchestration of resources by scheduling flows between services. Some of the proposals such as in [284] can enable service providers to predict the QoE of DASH-supported video streaming using fog nodes.…”

“…MEdia FOg Resource Estimation (MeFoRE) is one of the QoE-based framework proposed by [278] to provide resource estimation at Fog and to enhance QoS in IoT environments. Zheng et al [284] propose a Fog-assisted Real-time QoE Prediction (FRQP) strategy to enable service providers to predict the QoE of DASH-supported video streaming using fog nodes. FRQP uses the network-measured traffic to observe packet header information of the video traffic.…”

“…QoE-aware Adaptive Streaming over Cloud/Fog computing QoE-based resource management [274], [276], [277], [304]- [306], QoE optimization with energy efficiency [285] Perform QoS/QoE-aware orchestration of resources by scheduling flows between services. Some of the proposals such as in [284] can enable service providers to predict the QoE of DASH-supported video streaming using fog nodes. QoE-driven/aware Management using ICN [279], [281], QoE-driven content caching and adaptation scheme over MEC-enabled ICN [303] Performing prefetching of video streams that enables ICN to compute the link resources availability and makes scheduling of data units dissemination called "chunks" to edge caches according to end-users' requests [307] or video prefetching at the network edge in order to achieve the users' QoE [302] users have access to fog services with low delay, QoE support, and without significant network overhead.…”

Packet Scheduling for Multiple‐Switch Software‐Defined Networking in Edge Computing Environment

“…Therefore, three OpenFlow switches are installed for collecting data, and one interface is used to be connected to the controller network. The network traffics are generated by the software tool "iPerf" [29]. One host is selected as the server and another as the terminal by iPerf.…”