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.

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.

The paper presents a communication model of a set of heterogeneous processors interconnected via a switch-enabled Ethernet network. The goal of the model is to accurately predict the contribution of communication operations into the total execution time of parallel applications running on the platform. The presented model takes into account the impact of the heterogeneity of processors on the performance of communication operations. In this paper, we give analytical models for a single point-to-point communication, multiple independent point-to-point communications, multiple one-to-many point-to-point communications, and for a broadcast. Experimental results are presented demonstrating the accuracy of the analytical models

The paper presents novel content-aware adaptation schemes for QoE optimized HTTP-based adaptive video streaming services. In recent years, content type has been investigated and proofed to have a deep influence on user's acceptable quality of experience (QoE). However, most of the existing HTTP-based adaptive video strategies only conduct bitrate switching according to network resources without a consideration of video content types. This may cause a non-optimal network resource allocation which may not achieve the optimized QoE for delivered video streaming services. In this paper, we proposed two content-aware adaptation schemes, named as Content-aware Probe and Adapt (C-PANDA) and Content-aware Dynamic Programming (C-DP), which are able to decide the video bitrate for the next video segment, based on not only the available bandwidth, the existing buffer capacity, but also the video content type. We implemented both content-aware adaptation algorithms in player prototypes based on libdash 3.0 library. Preliminary results show that content-aware adaptation schemes achieve better QoE when compared with conventional PANDA scheme. The proposed schemes are able to optimize QoE according to contents. They can achieve higher than acceptable QoE for most attractive contents such as those with high motion intensity to improve user experience for the most interesting scenes; select same video representations for segments belonging to a scene to avoid quality oscillation; and reserve buffer resource during low motion scenes to avoid stalling for the following high motion scenes. The work will be helpful in designing QoE-aware DASH schemes in the future.

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.