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The price-anticipating Kelly mechanism (PAKM) is one of the most extensively used strategies to allocate divisible resources for strategic users in communication networks and computing systems. The users are deemed as selfish and also benign, each of which maximizes his individual utility of the allocated resources minus his payment to the network operator. However, in many applications a user can use his payment to reduce the utilities of his opponents, thus playing a misbehaving role. It remains mysterious to what extent the misbehaving user can damage or influence the performance of benign users and the network operator. In this work, we formulate a non-cooperative game consisting of a finite amount of benign users and one misbehaving user. The maliciousness of this misbehaving user is captured by his willingness to pay to trade for unit degradation in the utilities of benign users. The network operator allocates resources to all the users via the price-anticipating Kelly mechanism. We present six important performance metrics with regard to the total utility and the total net utility of benign users, and the revenue of network operator under three different scenarios: with and without the misbehaving user, and the maximum. We quantify the robustness of PAKM against the misbehaving actions by deriving the upper and lower bounds of these metrics. With new approaches, all the theoretical bounds are applicable to an arbitrary population of benign users. Our study reveals two important insights: 1) the performance bounds are very sensitive to the misbehaving user’s willingness to pay at certain ranges and 2) the network operator acquires more revenues in the presence of the misbehaving user which might disincentivize his countermeasures against the misbehaving actions. [ABSTRACT FROM AUTHOR]

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the “least” information from content providers. [ABSTRACT FROM AUTHOR]

Cloud gaming has become a new trend for gamers to access high-end video games. By rendering games in the remote cloud and streaming video scenes to the users, games can be played anywhere, anytime, on any device (e.g., smartphones, tablets, or personal computers). In this paper, we address the problem of achieving cost-effective adaptive cloud gaming in geo-distributed data centers from the perspective of cloud gaming service providers (CGSPs). Unlike previous work, we consider a cloud gaming system supported with the adaptive streaming technology. Our purpose is to minimize the overall service cost for CGSPs, by adaptively adjusting the selection of data centers, virtual machine allocation and video bitrate configuration for each user. Meanwhile, we also need to ensure good-enough quality of experience (QoE) for gamers. To this objective, we formulate the problem into a constrained stochastic optimization problem, and apply the Lyapunov optimization theory to drive the corresponding online strategy with provable upper bounds. Due to the diverse QoE requirements of video games, we also take the difference among game genres into account during the algorithm design. Finally, we conduct extensive trace-driven simulations to evaluate the effectiveness of our algorithm and our results show that our proposed algorithm can achieve significant gain over other alternative approaches. [ABSTRACT FROM AUTHOR]

Video streaming service in wireless networks is increasingly using dynamic selection of video bit-rates to provide a high quality of user experience (QoE). The bit-rate switching mechanism, performed at client side, plays a key role in determining QoE metrics. In this paper, we present the first analytical framework to compute starvation probability of playout buffer, continuous playback time and mean video quality, given the bit-rate switching logics. Wireless channel is modeled as a continuous time Markov process, and playout buffer is modeled as a fluid queue with Markov modulated fluid arrival. We construct a set of ordinary differential equations (ODEs) to characterize the dynamics of starvation probability and expected continuous playback time with regard to buffer length, and simple models to analyze mean bit-rate for different bit-rate switching algorithms. Our framework is very general in that by adding appropriate parameters, it can be utilized to predict the QoE metrics of dynamic adaptive streaming with a variety of features: i) buffer-aware bit-rate switching ii) (im)patience of the user, and iii) receiver-side flow control. [ABSTRACT FROM AUTHOR]

Currently, video-on-demand (VoD) streaming over Internet is a popular application. Because of the rapidly growing video population and user population, how to maintain high user quality of experience (QoE) with low cost is a challenging problem for Internet video streaming providers. A promising technique to potentially benefit VoD streaming system is network coding. A number of recent works studied how to use network coding to simplify chunk scheduling strategy to enhance VoD streaming performance. Most of these works only covered extreme cases of pure coding or pure chunk scheduling, emphasizing implementation, and experimentation in peer-to-peer (P2P) scenario without analytically evaluating the realizable performance gains explicitly. In this paper we discuss the strength and weakness of a family of coding strategies for CDN-based VoD streaming systems. The coding schemes are characterized by block sizes while the chunk scheduling strategy is characterized by the order to download chunks. Both pure coding strategy and pure chunk scheduling strategy are special cases of this family of strategies. We then propose a model to evaluate the benefits brought by each strategy. Basically, the coding scheme with larger block size gives more streaming and scheduling benefits with the cost of heavier overheads (e.g., encoding and decoding). System designers can take advantage of our model to balance the tradeoff between coding gain and coding overheads. [ABSTRACT FROM PUBLISHER]