Data traffic from mobile devices experiences unprecedented growth, which current cellular network capacities cannot sustain. Traffic offloading to other type of networks, such as WiFi, can be used to reduce load in cellular networks. In this paper, we propose a novel solution, which unlike other existing methodologies, implements tight cooperation with the cellular network to optimize traffic offloading. The cellular network provides information about channel usage statistics, user mobility patterns, available resources and other parameters. The offloading decisions aim at optimizing the balance between user application requirements and availability of network resources. The validation results, obtained from NS-3 simulations, confirm effectiveness of the proposed solution in balancing cellular traffic load while ensuring QoS.

/pdf/network-assisted-offloading-for-mobile-cloud-applications-3wlb4wnh9y.pdf

Network-assisted offloading for mobile cloud applications

With the widespread adoption of mobile Internet, the process of streaming video has become varied and complex. A diversity of factors affect the way we perceive quality in video streaming (also known as 'quality of experience', or QoE), involving far more than the individual video and network characteristics. Quality is affected by the overall delivery context, terminal specifications but also human factors. It is thus very hard to control the streaming system as a whole, targeting QoE rather than the tuning of individual factors. To better understand the non-obvious relation between network parameters and the resulting video quality, herein we present an experimental assessment of a representative video streaming platform, Skype. We find that simple QoE-management heuristics are only effective in very specific cases (for instance in 'head & shoulder' video types), which suggests that a more human-centric QoE management will be required to further improve video delivery.

Quality of experience management for video streams: the case of Skype

Congestion control is essential for the stability of the Internet and the corresponding algorithms are commonly evaluated for interoperability based on flow-rate fairness. In contrast, video conferencing software such as Zoom uses custom congestion control algorithms whose fairness behavior is mostly unknown. Aggravatingly, video conferencing has recently seen a drastic increase in use - partly caused by the COVID-19 pandemic - and could hence negatively affect how available Internet resources are shared. In this paper, we thus investigate the flow-rate fairness of video conferencing congestion control at the example of Zoom and influences of deploying AQM. We find that Zoom is slow to react to bandwidth changes and uses two to three times the bandwidth of TCP in low-bandwidth scenarios. Moreover, also when competing with delay aware congestion control such as BBR, we see high queuing delays. AQM reduces these queuing delays and can equalize the bandwidth use when used with flow-queuing. However, it then introduces high packet loss for Zoom, leaving the question how delay and loss affect Zoom's QoE. We hence show a preliminary user study in the appendix which indicates that the QoE is at least not improved and should be studied further.

Video Conferencing and Flow-Rate Fairness: A First Look at Zoom and the Impact of Flow-Queuing AQM

Skype nodes generate a substantial part of real-time bi-directional video traffic nowadays. Employing a range of adaptive mechanisms, the application configures video streaming to meet the requirements of the communication and constraints of the underlying network. While other related works focus on passive network monitoring of Skype data flows, this paper studies Skype as a point-to-point streaming engine of video, beyond standard video calling. The emphasis is on the objective video quality as perceived by viewers. We built a testbed to generate network perturbations and stream certain videos between Skype nodes. We examine how network impairments affect objective metrics (i.e. PSNR and SSIM index) of the video and Skype's ability to reconstruct the original sample. The results suggest that Skype is weak at delivering good quality for high motion videos and that it is slow at recovering after a long period of high packet loss.

Can Skype be used beyond video calling

Congestion control is essential for the stability of the Internet and the corresponding algorithms are commonly evaluated for interoperability based on flow-rate fairness. In contrast, video conferencing software such as Zoom uses custom congestion control algorithms whose fairness behavior is mostly unknown. Aggravatingly, video conferencing has recently seen a drastic increase in use – partly caused by the COVID-19 pandemic – and could hence negatively affect how available Internet resources are shared. In this paper, we thus investigate the flow-rate fairness of video conferencing congestion control at the example of Zoom and influences of deploying AQM. We find that Zoom is slow to react to bandwidth changes and uses two to three times the bandwidth of TCP in low-bandwidth scenarios. Moreover, also when competing with delay aware congestion control such as BBR, we see high queuing delays. AQM reduces these queuing delays and can equalize the bandwidth use when used with flow-queuing. However, it then introduces high packet loss for Zoom, leaving the question how delay and loss affect Zoom’s QoE. We hence show a preliminary user study in the appendix which indicates that the QoE is at least not improved and should be studied further.

In this paper, we study Skype video call in LAN, WAN, and WiMAX. Our main interest is on traffic characteristics and user experience. Using the standard Foreman video sequence (320×240, 30fps, 8 second), we show that a Skype video call can adapt its source rate from < 5kBps up to ∼60kBps. The maximum achievable MOS (Mean Opinion Score) and EFR (Effective Frame Rate) is about 3.5 and 20 fps, respectively. Our experimental results also show that source rate is the dominating factor in determining both traffic characteristics and user experience of a Skype video call; while end-to-end delay or transport layer protocol, e.g. TCP or UDP, plays very little role. We also study the minimum RSSI requirement in a typical WiMAX network for achieving various levels of user experience: good, fair, and low, and derive the explicit logarithmic functions using the curve fitting technique to predict MOS and mean packet inter-arrival time (PIT) based on source rate.

Jing Zhu

Papers

On traffic characteristics and user experience of Skype video call