The explosion of enhanced applications such as live video streaming, video gaming and Virtual Reality calls for efforts to optimize transport protocols to manage the increasing amount of data traffic on future 5G networks. Through bandwidth aggregation over multiple paths, the Multi-Path Transmission Control Protocol (MPTCP) can enhance the performance of network applications. MPTCP can split a large multimedia flow into subflows and apply a congestion control mechanism on each subflow. Segment Routing (SR), a promising source routing approach, has emerged to provide advanced packet forwarding over 5G networks. In this paper, we explore the utilization of MPTCP and SR in SDN-based networks to improve network resources utilization and end-user's QoE for delivering multimedia services over 5G networks. We propose a novel QoE-aware, SDN-based MPTCP/SR approach for service delivery. In order to demonstrate the feasibility of our approach, we implemented an intelligent QoE-centric Multipath Routing Algorithm (QoMRA) on an SDN source routing platform using Mininet and POX controller. We carried out experiments on Dynamic Adaptive video Steaming over HTTP (DASH) applications over various network conditions. The preliminary results show that, our QoEaware SDN-based MPTCP/SR scheme performs better compared to the conventional TCP approach in terms of throughput, link utilization and the end-user's QoE.

In this paper, we present a novel QoE-aware SDN/NFV system by utilizing and integrating Multi-path TCP (MPTCP) and Segment Routing (SR) paradigms. We propose a QoE-based Multipath Source Routing (QoEMuSoRo) algorithm that achieve an optimized end-to-end QoE for the end-user by forwarding MPTCP subflows using SR over SDN/NFV. We implement and validate the proposed scheme through DASH experiments using Mininet and POX controller. To demonstrate the effectiveness of our proposal, we compare the performance of our QoE-aware MPTCP SDN/NFV SR-based proposal, the MPTCP and regular TCP in terms of system throughput and the end-user's QoE. Preliminary results shows that, our approach outperforms the other aforementioned methods.

A new reference-free, objective, video quality prediction model that takes into account video content type to predict the quality of streamed high efficiency video coding (HEVC) encoded video sequences is proposed. Research has shown that for the same encoder settings and network quality of service (NQoS), the video quality differs for different types of video content. This indicates that, in addition to encoder settings and NQoS, there may be other key parameters that impact video quality. In this work, we hypothesized that video content type is one of the key parameters that may impact the quality of streamed videos. Based on this assertion, temporal information is extracted from the motion vector (MV) information inherent in the encoded video bitstreams and spatial information is extracted from the quantisation parameter (QP) and the number of bits (Bits) of coded intra (I) and predictive (P) frames to develop a metric that quantifies the content type of different video sequences. The content type metric is subsequently used together with encoding QP setting and network packet loss rate (PLR) to develop a reference-free objective video quality prediction model for streamed HEVC encoded video sequences. This model has an accuracy of 92% when the model predicted values of sequences not used in model derivation are compared with mean opinion score (MOS) obtained through subjective method.Index Terms-Content type, crowdsourcing, high efficiency video coding (HEVC), mean opinion score (MOS), motion activity, motion estimation, motion vector (MV), picture complexity, quality of experience (QoE).

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HTTP adaptive streaming (HAS) has become the de-facto standard for video streaming to ensure continuous multimedia service delivery under irregularly changing network conditions. Many studies already investigated the detrimental impact of various playback characteristics on the Quality of Experience of end users, such as initial loading, stalling or quality variations. However, dedicated studies tackling the impact of resolution adaptation are still missing. This paper presents the results of an immersive audiovisual quality assessment test comprising 84 test sequences from four different video content types, emulated with an HAS adaptation mechanism. We employed a novel approach based on systematic creation of adaptivity conditions which were assigned to source sequences based on their spatio-temporal characteristics. Our experiment investigates the resolution switch effect with respect to the degradations in MOS for certain adaptation patterns. We further demonstrate that the content type and resolution change patterns have a significant impact on the perception of resolution changes. These findings will help develop better QoE models and adaptation mechanisms for HAS systems in the future.