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Abstract-With the confluence of the growing market for mobile Internet devices, and users' expectations of instant access to high-quality multimedia content, the delivery of video over wireless networks has become the challenge of the decade. Dynamic Adaptive Streaming over HTTP (DASH) and WebRTC are new and evolving standards that have been developed specifically to meet this demand and enable a high-quality experience for mobile users of video on demand and real time communication services, respectively. However, there has been no systematic study of how these services are experienced by users in a realistic mobile setting. In this work, we describe measurements collected from DASH and WebRTC implementations while moving at walking speeds through an 802.16e WiMAX network. Using data from the application, network, and physical layers, in different wireless environments, we identify characteristics of the cellular data network that directly impact the quality of video service, and suggest areas for further improvement.

With the increasing interest in the use of millimeter wave bands for 5G cellular systems comes renewed interest in resource sharing. Properties of millimeter wave bands such as massive bandwidth, highly directional antennas, high penetration loss, and susceptibility to shadowing, suggest technical advantages to spectrum and infrastructure sharing in millimeter wave cellular networks. However, technical advantages do not necessarily translate to increased profit for service providers, or increased consumer surplus. In this paper, detailed network simulations are used to better understand the economic implications of resource sharing in a vertically differentiated duopoly market for cellular service. The results suggest that resource sharing is less often profitable for millimeter wave service providers compared to microwave cellular service providers, and does not necessarily increase consumer surplus.

In this work, we provide a methodology to analyze optimal adaptation policies for scalable video delivery in mobile environments. Typically, download policies for adaptive video are tuned to very specific system settings. The aim of this work is not to propose a new policy, but instead to understand how the optimal policy changes according to the operating environment and the system characteristics of a mobile video client. Armed with this insight, we can design or adapt policies for SVC adaptive video delivery for a broader range of settings.Using a semi-Markov decision process (SMDP), we find optimal video retrieval policies for a single user, subject to different limits on buffer capacity and different wireless environments. We apply a decision tree classifier to the output of the SMDP to derive simple approximate policies for 55 scenarios and use these to derive high-level rules on the relationship between optimal download policy and the underlying channel settings. For example, we show that the optimal policy is more conservative in slowly varying channels, and becomes more greedy in fast changing channels, and that instantaneous channel state is relevant to the decision-making process only in a setting with a very limited buffer capacity and slow-varying channel.