23 research outputs found
A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks
HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for
video delivery in mobile networks. Over the past few years, several HAS
algorithms have been introduced in order to improve user quality-of-experience
(QoE) by bit-rate adaptation. Their difference is mainly the required input
information, ranging from network characteristics to application-layer
parameters such as the playback buffer. Interestingly, despite the recent
outburst in scientific papers on the topic, a comprehensive comparative study
of the main algorithm classes is still missing. In this paper we provide such
comparison by evaluating the performance of the state-of-the-art HAS algorithms
per class, based on data from field measurements. We provide a systematic study
of the main QoE factors and the impact of the target buffer level. We conclude
that this target buffer level is a critical classifier for the studied HAS
algorithms. While buffer-based algorithms show superior QoE in most of the
cases, their performance may differ at the low target buffer levels of live
streaming services. Overall, we believe that our findings provide valuable
insight for the design and choice of HAS algorithms according to networks
conditions and service requirements.Comment: 6 page
Distributed no-regret edge resource allocation with limited communication
To accommodate low latency and computation-intensive services, such as the
Internet-of-Things (IoT), 5G networks are expected to have cloud and edge
computing capabilities. To this end, we consider a generic network setup where
devices, performing analytics-related tasks, can partially process a task and
offload its remainder to base stations, which can then reroute it to cloud
and/or to edge servers. To account for the potentially unpredictable traffic
demands and edge network dynamics, we formulate the resource allocation as an
online convex optimization problem with service violation constraints and allow
limited communication between neighboring nodes. To address the problem, we
propose an online distributed (across the nodes) primal-dual algorithm and
prove that it achieves sublinear regret and violation; in fact, the achieved
bound is of the same order as the best known centralized alternative. Our
results are further supported using the publicly available Milano dataset
Traffic Profiling for Mobile Video Streaming
This paper describes a novel system that provides key parameters of HTTP
Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A
non-intrusive traffic profiling solution is proposed that observes packet flows
at the transmit queue of base stations, edge-routers, or gateways. By analyzing
IP flows in real time, the presented scheme identifies different phases of an
HAS session and estimates important application-layer parameters, such as
play-back buffer state and video encoding rate. The introduced estimators only
use IP-layer information, do not require standardization and work even with
traffic that is encrypted via Transport Layer Security (TLS). Experimental
results for a popular video streaming service clearly verify the high accuracy
of the proposed solution. Traffic profiling, thus, provides a valuable
alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order
to perform efficient network optimization for video streaming.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of
IEEE ICC'1
Stochastic analysis of energy savings with sleep mode in OFDMA wireless networks
International audienceThe issue of energy efficiency (EE) in Orthogonal Frequency-Division Multiple Access (OFDMA) wireless networks is discussed in this paper. Our interest is focused on the promising concept of base station (BS) sleep mode, introduced recently as a key feature in order to dramatically reduce network energy consumption. The proposed technical approach fully exploits the properties of stochastic geometry, where the number of active cells is reduced in a way that the outage probability, or equivalently the signal to interference plus noise (SINR) distribution, remains the same. The optimal EE gains are then specified with the help of a simplified but yet realistic BS power consumption model. Furthermore, the authors extend their initial work by studying a non-singular path loss model in order to verify the validity of the analysis and finally, the impact on the achieved user capacity is investigated. In this context, the significant contribution of this paper is the evaluation of the theoretically optimal energy savings of sleep mode, with respect to the decisive role that the BS power profile plays
Stochastic multidimensional optimization techniques for planning of wireless communication networks
Over the last years, the application of stochastic optimization techniques in order to facilitate the solution of problems that involve network planning of wireless systems has become very popular. The main concept behind this approach is their ability to offer quickly near to optimal solutions for complex multi-objective and constrained problems. In the framework of this thesis, the process of 3G network planning and optimization is initially examined, along with the utilization of advanced techniques, such as smart antennas and MIMO systems. A realistic simulator has been developed for this reason, which comprises the main core of the planning and radio resource management process. The presented results indicate that network performance can improve significantly, while the trade-off balance between multiple and conflicting objectives is adjusted in a controllable manner. Therefore, two basic problems of terrestrial digital video broadcasting (DVB-T) network planning are analyzed and solved. Specifically, the procedure of allocating frequency channels to the defined service areas is firstly conducted with the implementation of stochastic algorithms. The transition from analogue to digital television is then investigated in detail. In any case, when the proper mathematical model is provided, the stochastic optimization algorithms prove to be a very powerful tool for the computationally complex and challenging process of network planning