To understand VoLTE performance in a commercial deployment, in this paper we conduct the first comprehensive performance characterization of commercially deployed VoLTE, and compare with legacy call and over-the-top (OTT) VoIP call. We confirm that VoLTE excels in most metrics such as audio quality, but its call reliability still lags behind legacy call for all the three major U.S. operators. We propose an on-device VoLTE problem detection tool, which can capture new types of problems concerning audio quality with high accuracy and minimum overhead, and perform stress testing on VoLTE call's reliability. We discover 3 instances of problems in the early deployment of VoLTE lying in the protocol design and implementation. Although the identified problems are all concerned with the immature LTE coverage in the current deployment, we find that they can cause serious impairment on user experience and are urgent to be solved in the developing stage. For example, one such instance can lead to up to 50-second-long muting problem during a VoLTE call! We perform in-depth cross-layer analysis and find that the causes are rooted in the lack of coordination among protocols designed for different purposes, and invalid assumptions made by protocols used in existing infrastructure when integrated with VoLTE. We summarize learnt lessons and suggest solutions.

Smartphones have become increasingly prevalent and important in our daily lives. To meet users' expectations about the Quality of Experience (QoE) of mobile applications (apps), it is essential to obtain a comprehensive understanding of app QoE and identify the critical factors that affect it. However, effectively and systematically studying the QoE of popular mobile apps such as Facebook and YouTube still remains a challenging task, largely due to a lack of a controlled and reproducible measurement methodology, and limited insight into the complex multi-layer dynamics of the system and network stacks.

To conserve power while ensuring good performance on resource-constrained mobile devices, devices transition between different Radio Resource Control (RRC) states in response to network traffic and according to parameters specific to network operators. As RRC states significantly affect application power consumption and performance, it is important to understand how RRC state timers interact with network traffic patterns. In this paper, we show that the impact of RRC states on performance is significantly more complex and diverse than found in previous work. To do so, we introduce an open-source tool that allows the impact of RRC states on network and application performance to be measured in a robust and accurate manner on unmodified user devices, and deploy the tool in 23 countries around the world to test a broad range of cellular network technologies. We detect previously unknown performance problems which increase network latencies by up to several seconds and for LTE, can increase packet losses by an order of magnitude. Through an in-depth cross-layer analysis of several carriers, we examine the lower-layer causes of these problems. We determine that the highly complex state transitions of certain carriers, and in particular poor interactions between state demotions and network traffic, can lead to substantial, unexpected latencies.

T-Mobile USA is looking at all possible ways to optimize the quality of user experience (QoE) for our customers in while balanceing with our network resources and device energy consumptions needs. In the mobile smart phone world, users often have a the misperception that people often held about use experience is the faster the network speed is and, the faster the phone CPU speed is;, the better the quality of experience (QoE) will beis. On the contrary, we intend to use three practical examples to unveil the mystery behind the mobile network black box and illustrate it is actually the end-to-tend (E2E) latency which impacts the QoE of one of the most commonly used data services -- mobile browsing. A lengthy E2E latency due to rarely-understood and hard-to-uncovered phone or network issues may render a very poor browsing page loading speed time even if you have a super hero dual core smart phone with the fastest 4G network speed. In order to understand the E2E latency better in the transient wireless channel conditions and optimize data QoE in a much fastermore expedient way, we advocate the use of the cCross-device/network evaluation methodology. Finally, we presented a few research questions that we are facing and to solicit the research community's input.

The increasing popularity of wireless fidelity (Wi-Fi) networks at home, in public areas and in the enterprise motivates extensive modeling and analysis of their performance measures, such as network capacity and quality of service (QoS) capabilities. Some of the easier performance problems can be solved by analytical modeling methods, but most of the complicated ones, involving several design parameters from multiple layers, can only be answered through simulation studies that are typically implicit and less suitable for design and optimization. Hence, we believe it is crucial to obtain explicit mathematic models, that are effective in representing system behavior and a basis for performance optimization. Here, we first advocate the application of formal empirical modeling techniques to performance studies of Wi-Fi networks, in order to find usable, if approximate, closed-form mathematical models. Subsequently, by applying these metamodeling techniques, we perform a case study with very useful results: our VoWiFi (voice over Wi-Fi) admission capacity metamodel gives a much tighter bound than those existing in the literature and leads to a more effective admission control scheme. Our work, therefore, points out a new direction for future performance studies of Wi-Fi networks.

The increasing popularity of Wireless Fidelity (Wi-Fi) networks at home, in public areas and in the enterprise motivates extensive modeling and analysis of their performance measures, such as network capacity, resource requirements and quality of service (QoS) capabilities. Some of the easier performance problems can be solved by analytical modeling methods, but most of the complicated ones, involving too many factors from multiple layers, can only be answered through validated simulation models. However, an explicit mathematic model is always the most effective way to represent the system behavior and the most convenient basis for performance optimization. Here, we first advocate the application of metamodeling techniques to performance studies of Wi-Fi networks, in order to find usable, if approximate, closed-form mathematical models. Subsequently, we formulate a general metamodeling framework for Wi-Fi networks. Our results in two relevant case studies, after applying this framework, support the validity of our metamodeling methodology: our capacity metamodel for 802.11 Distributed Coordination Function (DCF) is validated by a well-known analytical model and displays an interesting log-linear relationship between capacity and number of users; our voice over Wi-Fi admission capacity metamodel gives a much tighter bound than bounds existing in the literature and composes a more practical admission control scheme. Our work, therefore, points out a new direction for future performance studies of Wi-Fi networks.

Rapid deployment of IEEE 802.11 wireless local area networks (WLANs) and their increasing quality of service (QoS) requirements motivate extensive performance evaluations of the upcoming 802.11e QoS-aware enhanced distributed coordination function (EDCA). Most of the analytical studies up-to-date have been based on one of the three major performance models in legacy distributed coordination function analysis, requiring a large degree of complexity in solving multidimensional Markov chains. Here, we expose the common guiding principle behind these three seemingly different models. Subsequently, by abstracting, unifying, and extending this common principle, we propose a new unified performance model and analysis method to study the saturation throughput and delay performance of EDCA, under the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. This unified model combines the strengths of all three models, and thus, is easy to understand and apply; on the other hand, it helps increase the understanding of the existing performance analysis. Despite its appealing simplicity, our unified model and analysis are validated very well by simulation results. Ultimately, by means of the proposed model, we are able to precisely evaluate the differentiation effects of EDCA parameters on WLAN performance in very broad settings, a feature which is essential for network design.

Performance evaluation of the upcoming 802.11e QoS-aware enhanced distributed channel access (EDCA) in WLANs has drawn extensive attention. The foundations for most of the studies are three major performance models in legacy distributed coordination function (DCF) analysis. In this paper, we first expose the common guiding principle behind these three seemingly different models. Subsequently, we propose a new unified performance model and analysis method to study the saturation throughput and delay performance of EDCA, under the assumption of a finite number of users and ideal channel conditions in a single-hop WLAN. This unified model is easy to understand and apply and helps increase the understanding of the existing performance analysis. In addition to its appealing simplicity, our unified model and analysis are validated well by simulation results. And ultimately, by means of the proposed model, we are able to evaluate the differentiation effects of EDCA parameters on WLAN performance in very broad settings, a feature which is essential for network design.

The enhanced distributed coordination function (EDCF) is one of the main features of 802.11e quality of service (QoS) enhancement over legacy 802.11 distributed coordination function (DCF). In order to control network performance, a network designer needs to understand how the EDCF parameters affects the QoS performance explicitly. However, we are unaware of any publications regarding EDCF analysis that includes all of the protocol details. Most evaluations only use numerical results. We propose a simple but extremely accurate analytical model to compute the saturation throughput performance of 802.11e EDCF, with the assumption of ideal channel conditions and same different inter frame space (DIFS). The analysis consists of two cases. The first case demonstrates how different nodes can achieve differentiate access in uplink. In the second scenario, there are multiple flows in one node, and they compete for the channel in downlink. With the help of the analysis, we show how to design a distributed MAC weighted round robin (WRR) packet scheduler to emulate an ideal network layer scheduler. Simulation comparison indicates that the MAC layer scheduler can achieve higher utilization, smaller delay than the network layer WRR and bandwidth ratio as wanted.