Accurate cross-layer information is very useful to optimize mobile networks for specific applications. However, providing application-layer information to lower protocol layers has become very difficult due to the wide adoption of end-to-end encryption and due to the absence of cross-layer signaling standards. As an alternative, this paper presents a traffic profiling solution to passively estimate parameters of HTTP Adaptive Streaming (HAS) applications at the lower layers. By observing IP packet arrivals, our machine learning system identifies video flows and detects the state of an HAS client's play-back buffer in real time. Our experiments with YouTube's mobile client show that Random Forests achieve very high accuracy even with a strong variation of link quality. Since this high performance is achieved at IP level with a small, generic feature set, our approach requires no Deep Packet Inspection (DPI), comes at low complexity, and does not interfere with end-to-end encryption. Traffic profiling is, thus, a powerful new tool for monitoring and managing even encrypted HAS traffic in mobile networks.
Closer to our work is the methodology presented in @cite_18 , where the target is to predict the class of the play-back buffer level during the video session by defining a set of buffer levels in seconds. Unlike in this work, our focus is the prediction of the buffer state, which is a more fundamental property of adaptive streaming clients.
@cite_18
2622055490 1579111450 2160624927 2106290740
Stalls during video playback are perhaps the most important indicator of a client's viewing experience. To provide the best possible service, a proactive network operator may therefore want to know the buffer conditions of streaming clients and use this information to help avoid stalls due to empty buffers. However, estimation of clients' buffer conditions is complicated by most streaming services being rate-adaptive, and many of them also encrypted. Rate adaptation reduces the correlation between network throughput and client buffer conditions. Usage of HTTPS prevents operators from observing information related to video chunk requests, such as indications of rate adaptation or other HTTP-level information. This paper presents BUFFEST, a novel classification framework that can be used to classify and predict streaming clients' buffer conditions from both HTTP and HTTPS traffic. To illustrate the tradeoffs between prediction accuracy and the available information used by classifiers, we design and evaluate classifiers of different complexity. At the core of BUFFEST is an event-based buffer emulator module for detailed analysis of clients' buffer levels throughout a streaming session, as well as for automated training and evaluation of online packet-level classifiers. We then present example results using simple threshold-based classifiers and machine learning classifiers that only use TCP IP packet-level information. Our results are encouraging and show that BUFFEST can distinguish streaming clients with low buffer conditions from clients with significant buffer margin during a session even when HTTPS is used. Network variations in video streaming require sufficient data to be prebuffered at the client prior to playout. This receiver buffer prevents the display process from starvation in case of network congestion. Prebuffering, however, is also responsible for the major part of the delay between requesting a media stream and playing it at the receiver. We show how adaptive media playout can be employed to reduce the delay introduced by the receiver buffer while preserving the same resilience against buffer underflow as in non-adaptive media playout. For adaptive media playout we adjust the playout speed of the media packets depending on the condition of the channel and the current client buffer fullness. We employ a two-state Markov channel model to analyze the buffer underflow-delay trade-off for our adaptive playout strategy and show that for typical parameters the average end-to-end delay can be reduced by 1 to 2 seconds. We study the optimal streaming of variable bit-rate (VBR) video over a random VBR channel. The goal of a streaming application is to enable the successful decoding of each video object before its displaying deadline is violated. Hence, we define the main performance metric of a streaming system as the probability of un-interrupted video presentation, or jitter-free probability. Previous literature has described solutions to estimate the jitter-free probability by assuming either independence in the encoded data process or simplistic channel models. In this work, we present a novel analytical framework, which requires only some basic statistical information of an arbitrary VBR channel, to bound the probability of jitter-free playout under the constraint of initial playout delay and receiver buffer size. Both the infinite and finite buffer cases are considered. This technique is then applied to investigate streaming over a wireless system modeled by an extended Gilbert channel with ARQ transmission control. Experimental results with MPEG-4 VBR encoded video demonstrates that the proposed analysis derives close bounds to the actual system performance. Finally, we show that the proposed analysis provides a theoretical foundation to quantify the tradeoffs between the initial playout delay, the receiver buffer size, and the jitter-free probability for a general class of VBR streaming over random VBR channels. When media is streamed over best-effort networks, media data is buffered at the client to protect against playout interruptions due to packet losses and random delays. While the likelihood of an interruption decreases as more data is buffered, the latency that is introduced increases. In this paper we show how adaptive media playout (AMP), the variation of the playout speed of media frames depending on channel conditions, allows the client to buffer less data, thus introducing less delay, for a given buffer underflow probability. We proceed by defining models for the streaming media system and the random, lossy, packet delivery channel. Our streaming system model buffers media at the client, and combats packet losses with deadline-constrained automatic repeat request (ARQ). For the channel, we define a two-state Markov model that features state-dependent packet loss probability. Using the models, we develop a Markov chain analysis to examine the tradeoff between buffer underflow probability and latency for AMP-augmented video streaming. The results of the analysis, verified with simulation experiments, indicate that AMP can greatly improve the tradeoff, allowing reduced latencies for a given buffer underflow probability.