Performance Modeling of Mobile Video Streaming

Abstract

In past two decades, there has been a trend to move from traditional television to Internet-based video services. With video streaming becoming one of most popular applications in Internet and current state of art in media consumption, quality expectations of consumers are increasing. Low quality videos are no longer considered acceptable in contrast to some years ago due to increased sizes and resolution of devices. If high expectations of users are not met and a video is delivered in poor quality, they often abandon service. Therefore, Internet Service Providers (ISPs) and video providers are facing challenge of providing seamless multimedia delivery in high quality. Currently, during peak hours, video streaming causes almost 58\% of downstream traffic on Internet. With higher mobile bandwidth, mobile video streaming has also become commonplace. According to 2019 Cisco Visual Networking Index, in 2022 79% of mobile traffic will be video traffic and, according to Ericsson, by 2025 video is forecasted to make up 76% of total Internet traffic. Ericsson further predicts that in 2024 over 1.4 billion devices will be subscribed to 5G, which will offer a downlink data rate of 100 Mbit/s in dense urban environments. One of most important goals of ISPs and video providers is for their users to have a high Quality of Experience (QoE). The QoE describes degree of delight or annoyance a user experiences when using a or application. In video streaming QoE depends on how seamless a video is played and whether there are stalling events or quality degradations. These characteristics of a transmitted video are described as application layer Quality of Service (QoS). In general, QoS is defined as the totality of characteristics of a telecommunications that bear on its ability to satisfy stated and implied needs of user of service by ITU. The network layer QoS describes performance of network and is decisive for application layer QoS. In Internet video, typically a buffer is used to store downloaded video segments to compensate for network fluctuations. If buffer runs empty, stalling occurs. If available bandwidth decreases temporarily, video can still be played out from buffer without interruption. There are different policies and parameters that determine how large buffer is, at what buffer level to start video, and at what buffer level to resume playout after stalling. These have to be finely tuned to achieve highest QoE for user. If bandwidth decreases for a longer time period, a limited buffer will deplete and stalling can not be avoided. An important research question is how to configure buffer optimally for different users and situations. In this work, we tackle this question using analytic models and measurement studies. With HTTP Adaptive Streaming (HAS), video players have capability to adapt video bit rate at client side according to available network capacity. This way depletion of video buffer and thus stalling can be avoided. In HAS, quality in which video is played and number of quality switches also has an impact on QoE. Thus, an important problem is adaptation of video streaming so that these parameters are optimized. In a shared WiFi multiple video users share a single bottleneck link and compete for bandwidth. In such a scenario, it is important that resources are allocated to users in a way that all can have a similar QoE. In this work, we therefore investigate possible fairness gain when moving from network fairness towards application-layer QoS fairness. In mobile scenarios, energy and data consumption of user device are limited resources and they must be managed besides QoE. Therefore, it is also necessary, to investigate solutions, that conserve these resources in mobile devices. But how can resources be conserved without sacrificing application layer QoS? As an example for such a solution, this work presents a new probabilistic adaptation algorithm that uses abandonment statistics for ts decision making, aiming at minimizing resource consumption while maintaining high QoS. With current protocol developments such as 5G, bandwidths are increasing, latencies are decreasing and networks are becoming more stable, leading to higher QoS. This allows for new real time data intensive applications such as cloud gaming, virtual reality and augmented reality applications to become feasible on mobile devices which pose completely new research questions. The high energy consumption of such applications still remains an issue as energy capacity of devices is currently not increasing as quickly as available data rates. In this work we compare optimal performance of different strategies for adaptive 360-degree video streaming.