In this paper, we develop an analytical framework to compute the Quality-of-Experience (QoE) metrics of video streaming in wireless networks. Our framework takes into account the system dynamics that arises due to the arrival and departure of flows. We also consider the possibility of users abandoning the system on account of poor QoE. Considering the coexistence of multiple services such as video streaming and elastic flows, we use a Markov chain based analysis to compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and bitrate switching. Our simulation results validate the accuracy of our model and describe the impact of scheduler at eNB on the QoE metrics.

We consider the status updates of a physical process over an unreliable channel. In this setting, one may not be able to reliably transmit the current state at all times. Instead, one is interested in the timeliness of the accurately received information. This is a setting for several cyber-physical system applications that require real-time monitoring and control. In this paper, we study periodic data transmission schemes at a single source which exploit the temporal correlation in the source messages. When the source has no feedback, it can periodically send the actual information, interspersed with differential messages. On the availability of receiver's feedback at the source, it can decide to send either the differential or the actual information at each transmission opportunity. For a fixed length coding, we show that the differential encoding improves the timeliness performance only if the receiver's feedback is available.

For timely sensor update, the traditional approach is to send new information at every available opportunity. Recent research has shown that with limited receiver feedback, sensors can improve the update timeliness by transmitting differential information for slowly varying correlated sources. One can elect to transmit either the actual or the differential state information based on a differential encoding threshold for a general Markov source. This threshold captures the natural trade-off between differential transmission opportunities and the coding gains. Using matrix-geometric method, we find the limiting age distribution for a Markov source as a function of the encoding threshold, from which several other performance metrics of interest, such as mean age, peak age, and the probability of decoding failure can be derived.

Video streaming over cellular network has become extremely popular in 4G and will be an integral part of future cellular networks. While most modern-day video clients continually adapt quality of video streams, they neither coordinate with network elements nor among each other. Consequently, a streaming client may quickly overload the cellular network, leading to poor Quality of Experience (QoE) for users in the network. Motivated by this problem, we present D-VIEWS - a scheduling paradigm that assures video bitrate stability of adaptive video streams while ensuring better system utilization. D-VIEWS only needs to be aware of the set of video bitrates and requires no changes to streaming clients and other network functions. Through simulations, we also study the performance of proportional fairness scheduler and D-VIEWS in the presence of user arrival and departure events.

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

For timely sensor update, the traditional approach is to send new information at every available opportunity. Recent research has shown that with limited receiver feedback, sensors can improve the update timeliness by transmitting differential information for slowly varying correlated sources. In general, correlated sources can elect to transmit actual or differential state information depending on the current state. This encoding scheme generalizes the actual and differential updates schemes. Using this generalized scheme, we quantify the timeliness gains for some example sources. Further, we show a stochastic ordering among the actual update, the differential update, and the generalized update schemes.

For many applications in sensor networks and cyber-physical systems, receiving timely information is of utmost importance. In this article, we study data transmission schemes for a single source, sending periodic updates to a receiver through an unreliable channel. We consider two schemes that exploit the temporal correlation in the source messages, to send differential information to the receiver. Taking advantage of the receiver feedback in the first scheme, the source can decide between the differential and the actual information, to be sent at each transmission opportunity. Contrastingly, in the second scheme without any feedback, the source periodically sends the actual information, interspersed with differential messages. We observe that the differential encoding improves the timeliness performance, only if the receiver feedback is available.

We study TCP performance over ON-OFF channels, i.e., channels which are busy/unavailable for transmissions intermittently. The data may also be subject to random errors. Such situations arise in cognitive radio networks when secondary users opportunistically use the channel when the licensed user is not using it. We use a Markov model to evaluate the TCP throughput and probability of retransmission timeout when the ON-OFF periods are exponentially distributed and then extend it to phase-type ON and phase-type OFF periods. We then generalize the Markovian model to more general ON-OFF duration distributions and also to multiple TCP connections.

We study a system of multiple routers traversed by multiple TCP connections using TCP New Reno, CUBIC and Compound. More than one router may be congested. To analyze this system we will use earlier theoretical models of TCP New Reno and CUBIC but develop a new model of TCP Compound experiencing random packet losses and queuing delays. We model the router queues as M/GI/1 queues with arrival rates controlled by TCP window flow control. We also look at an alternate approach assuming proportional fairness of TCP to find the throughputs of the multiple TCP connections. These approximations are validated through comparison with ns-2 simulations.

We develop a Markov model for a TCP CUBIC connection. Next we use it to obtain approximate expressions for throughput when there may be queuing in the network. Finally we provide the throughputs different TCP CUBIC and TCP NewReno connections obtain while sharing a channel when they may have different round trip delays and packet loss probabilities.