Context-aware mobility resource allocation for QoE-driven streaming services

Abstract

Streaming services come with their own challenges and technical issues that still need to be addressed for satisfying the target quality of experience (QoE) of the end-users in mobile environments. In this paper, we explore the idea of combining users' context information with the packed prefetching process features to enhance users' QoE in heterogeneous networks. More specifically, we propose a scheduling mechanism for video streaming traffic, in which the access to the network resources is restricted to users with a signal-to-noise plus interference ratio (SINR) above a given threshold. This scheme benefits from the fact that, as users are in permanent motion, they may experience different SINR values during the same video streaming session offering the opportunity to only schedule users with good channel conditions. The proposed scheduling approach (subsequently referred to as context-aware mode switching (CAMS)) not only allows to achieve overall network spectral efficiency improvement, but also guarantees fairness and QoE among users. Our simulation results show that CAMS achieves almost 1 bit per second per Hertz gain compared to the conventional scheduler (without CAMS), and up to 87% improvement in the probability of no starvations when users move at 40 kmph.

A survey on QoE-oriented wireless resources scheduling

“…The scheduling methods referred above perform resource allocation and/or prioritization at the wireless MAC/physical layer level, which is the scope of this work. Nevertheless, there are also QoE-aware algorithms that, although taking into consideration the wireless channel specificities, they permit to preserve an entirely standard and application-layer unaware physical/MAC operationfor instance, algorithms that run above the MAC layer and which arrange suitably the order of the data units delivered to this layer; more examples and details can be found in (Bianchi et al, 2010;Chen et al, 2015b;Radics et al, 2015;Borkowski et al, 2016;Eswara et al, 2016;Héder et al, 2016;Kumar et al, 2016;Tajima and Okabe, 2016;Triki et al, 2016;Zheng et al, 2017).…”

MoniSys: Sensing, Communication, and Video Analysis in UAV Monitoring System

“…• Challenge 1: How to profile the mobility impact and use the profile to maximize clients' QoE fairness in a mobile network? Most existing works [16][17][18][19][20][21][22][23] depend on off-the-shelf mechanisms to ensure the network performance, like QoE, by dividing bandwidth evenly among multiple clients' connections, which neglects the knowledge of clients. Clients with the same bandwidth may experience different viewing experiences in mobile video streaming applications because of clients' mobility profiles, e.g., speed, direction, and acceleration.…”

“…Mobile wireless network environment is heterogeneous due to the varied topologies and number of BSes, as well as the diverse clients' mobility patterns. The existing bandwidth allocation approaches to QoE improvement for video streaming applications [18][19][20][21][22][23] did not consider the robustness of the model. However, It is critical and valuable to build a model which can be adapted to various scenarios in the real world.…”