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.