QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach

In the last decade, empowered by the technological advancements of mobile devices and the revolution of wireless mobile network access, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a stable high-quality playback experience is compulsory to maximize the viewers’ Quality of Experience and the content providers’ profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Additionally, because of the instability of network condition and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally expensive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On demand renting of resources or inadequate resources reservation may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than the required, the extra resources will be wasted. In this thesis, we introduce a prediction-driven resource allocation framework, to maximize the QoE of viewers and minimize the resources allocation cost. First, by exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers’ proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation. Considering the complexity and infeasibility of our offline optimization to respond to the volume of viewing requests in real-time, we further extend our work, by introducing a resources forecasting and reservation framework for geo-distributed cloud sites. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers’ proximity, while creating a tradeoff between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site. The results showed that the predicted number of transcoding resources needed in each cloud site is close to the optimal number of transcoding resources

Evolução do comportamento dos usuários em serviços de streaming de vídeo em larga escala

Video streaming is responsible for most of the traffic flowing on the Internet nowadays, which leads to massive investments in the infrastructure from the main content providers like Netflix, Youtube, and Hulu. In the past few years, these investments generated multiple changes in the transmission infrastructure, improving the content delivery networks (CDN), enhancing the computer processing power and changing the transmission technology used. The ultimate goal of the content providers when making these investments is to transmit streaming video over the Internet with the best quality as possible to please their clients and stand out from their competitors. In this sense, it is necessary to have metrics capable of capturing the impact imposed on the servers, user behavior, and in the quality of the video to know how changes in the transmission infrastructure affect user engagement. In this context, in this work, we characterize the evolution of transmission infrastructure, user behavior, and quality of experience from live video streaming systems over the Internet, using for this purpose the logs generated from the transmission of the 2014 and 2018 FIFA World Cups that were transmitted by the biggest content provider located in Brazil. Some of the results in this work show that traffic has increased more than 300% and the average public increased about 175%. The arrival rate also changed and now it is more concentrated at the beginning of the 1st and 2nd half of the match. Besides that, user engagement was evaluated using different metrics and, in general, it has increased in the 2018 tournament for all metrics.Streaming de vídeo é o responsável pela maior parte do tráfego na Internet atualmente, o que gera investimentos massivos em infraestrutura por parte dos principais provedores de conteúdo como Netflix, Youtube e Hulu. Ao longo dos últimos anos, esses investimentos geraram diversas modificações na infraestrutura de transmissão como melhora nas redes de distribuição de conteúdo (CDN), aumento do poder computacional e mudanças na tecnologia de transmissão. O objetivo final dos provedores de serviço ao realizar todos esses investimentos é ser capaz de realizar uma transmissão com a maior qualidade possível para agradar seus clientes, de modo que suas plataformas se destaquem de seus concorrentes. Nesse sentido, é necessário ter métricas que representem questões como o impacto causado nos servidores por conta da quantidade grande de clientes, o padrão de comportamento desses clientes e a qualidade do vídeo que está sendo assistido, para que seja possível saber os efeitos das mudanças de infraestrutura no engajamento final do cliente. Nesse contexto, esse trabalho avalia a evolução da infraestrutura de transmissão, do comportamento do cliente e da experiência do usuário de sistemas de streaming ao vivo na Internet, utilizando como objeto de estudo os registros de acesso das transmissões das Copas do Mundo da FIFA de 2014 e 2018 realizadas pelo maior provedor de conteúdo do Brasil. Alguns dos resultados obtidos mostram que o tráfego gerado cresceu mais de 300% e o público médio cerca de 175% nos quatro anos entre os eventos. A taxa de chegada também mudou e passou a ser mais concentrada no início do 1º e 2º tempo do jogo. Além disso, em relação ao engajamento do usuário, foram avaliadas diferentes métricas e, em geral, o engajamento aumentou no torneio de 2018 para todas as métricas utilizadas

Characterizing QoE in Large-Scale Live Streaming

Understanding the impact of performance degradation on users' QoE during live Internet streaming is key to maximize the audience and increase content providers' revenues. It is known that some problems have a strong correlation with low QoE--e.g., users experiencing video stalls tend to leave video sessions earlier. It is, however, mostly unknown whether such observations hold for live streaming of large-scale events (e.g., the FIFA World Cup). Such events are particular due to the widespread interest in the streamed content, reaching an impressively high audience worldwide. We study whether and to what extent performance degradation during live streaming of large-scale events affects users' QoE. We leverage a unique dataset collected from a major content provider in South America during the 2014 FIFA Soccer World Cup. We first extract performance metrics from the logs: stream bitrate, bitrate switches, playback stalls, and playback startup latency. We then correlate these performance metrics with session duration, which we use as a QoE indicator. We confirm the strong correlations between the metrics and QoE indicators; in particular, frequent stalls are often accompanied by higher probability of early session termination. Moreover, we quantify how such correlations vary according to broadcast matches and client terminals. Some of our findings challenge intuition--e.g., we find that PC users seem more tolerant to problems than users on mobile terminals. Our results provide better understanding of user QoE and are an important step towards user QoE models in large-scale events

Learning for Network Applications and Control

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling