Dheeraj Puri Goswami

Bio: Dheeraj Puri Goswami is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Scheduling (computing) & Integer programming. The author has an hindex of 2, co-authored 4 publications receiving 7 citations.

Buffer aware three level scheduler for video streaming over LTE

Abstract: The ever increasing demand for high bandwidth, low latency multimedia applications on mobile devices is set to pose a considerable challenge on the bandwidth allocation and multiplexing mechanisms in LTE and future wireless networks. One of the most important factors that diminish the quality of viewing experience of delivered videos is frequent client side rebuffering events. This paper proposes a buffer-aware downlink scheduling framework (called BA-TLS) for streaming video applications. The problem of scheduling a set of video streams over LTE has first been formulated as an Integer Linear Programming (ILP) problem. It has been shown that a conventional dynamic programming solution for the ILP imposes prohibitive online overheads. Then, by utilizing analytical properties of the problem, a genetic algorithm based fast and efficient radio resource allocation heuristic has been implemented over the proposed framework. Experimental results reveal that the developed framework is able to provide significant reductions in rebuffering events and thus allow video transmission with high and acceptable quality of service.

Contrast various tests for primality

Abstract: With the increasing popularity and usage of cryptography and network security, prime generation and primality testing has become a significant issue. Various primality tests have been introduced in the past and we compare and contrast some popular primality tests and formulate our own results by implementing and testing them on a million numbers. Primality tests can be classified on the basis of whether they are general or specific, polynomial, deterministic or probabilistic and conditional or unconditional. We have considered AKS which is simultaneously general, polynomial, deterministic and unconditional while the other primality tests considered do not satisfy all these four properties simultaneously. Although AKS is an important algorithm theoretically, it is not used in practice due to it's poor performance versus the other primality test for large inputs. The results from our implementation show that Bailie-PSW performs the best while Fermat performs the poorest in probabilistic algorithms in terms of accuracy. AKS and Pocklington were found to perform as expected on the first five lakh numbers. Prime generation techniques are popular for generation of small primes. The most popular among these are the prime sieves. We implement, compare and contrast two of these namely, Sieve of Eratosthenes and Sieve of Sundaram on various ranges of numbers.

Supporting Tuple Space based Mobile Middleware over unreliable mobile infrastructures: Design and formal specifications

Abstract: Tuple Space based Mobile Middleware (TSMM) intends to facilitate coordination of different components (i.e. agents) of supported applications in multiple mobile, unreliable networks, which is a challenging problem. This paper proposes TSMM-specific discovery and communication mechanisms that enable TSMM to provide coordination over multiple networks. The paper also presents an approach of formalizing proposed mechanisms using Mobile UNITY.

A Buffer Aware Resource Allocation Framework for Video Streaming over LTE

Abstract: Latency sensitive high bandwidth multimedia data is expected to capture more than \(70\%\) of the available bandwidths in LTE and future wireless networks. This is expected to pose enormous challenges on the radio resource and management mechanisms in these networks. One of the most important factors that diminish the quality of viewing experience of delivered videos is frequent client side rebuffering events. This paper proposes a buffer-aware downlink scheduling framework (called BA-TLS) for streaming video applications. The problem of scheduling a set of video streams over LTE has first been formulated as an Integer Linear Programming (ILP) problem. It has been shown that a conventional Dynamic Programming (DP) solution for the ILP imposes prohibitive online overheads. Then, by utilizing analytical properties of the problem, a genetic algorithm based stochastic solution has been implemented. Further, a fast and efficient deterministic heuristic known as Proportionally Balanced Robustness-level Allocator (PBRA) has been implemented over the proposed framework. Experimental results show that while the optimal DP based solution performs significantly better at reducing rebuffering events with respect to the stochastic as well as the deterministic solutions, both of them incur much lower temporal overheads compared to DP. Further, it may be observed that although the stochastic and deterministic solutions are comparable in performance, the deterministic heuristic solution PBRA is about 300 to 600 times faster on average compared to its stochastic counterpart.

An Online Buffer-Aware Resource Allocation Algorithm for Multiuser Mobile Video Streaming

Abstract: Mobile video traffic has experienced exponential growth in recent years due to increasing prevalence of mobile devices and continuous advancement of wireless communications. However, network fluctuations often occur when transmitting video data, which greatly impact the quality of experience (QoE) of mobile users. Buffering technique has been widely adopted to mitigate the effect of network fluctuations on QoE for mobile users by prefetching certain video data to mobile devices in advance. However, mobile users often abort video watching before the videos are fully watched and such early departure behavior can result in considerable wastage of buffered video data. In this paper, we study buffer-aware resource allocation for multiuser mobile video streaming system by jointly considering video users early departure behavior and wireless channel fluctuation property. We formulate a stochastic optimization problem in which the objective is to reduce both the amount of wasted buffered data and the video re-buffering time. The problem is then transformed into a series of deterministic per-slot problems by using Lyapunov optimization theory. We propose an online buffer-aware resource allocation algorithm to solve the per-slot optimization problems. After proving the convexity of the per-slot optimization problem, we further design a fast-convergent iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) to perform each of the per-slot resource allocations. Simulation results show that our proposed algorithm has very low running time, near optimal performance, and is able to achieve a good trade-off between QoE improvement and data wastage reduction.

New properties of a composition of ordinary generating functions for primes

Abstract: In this paper, we consider properties of coefficients of a composition of ordinary generating functions. Using the notion of a composita, we get some new properties for this composition. Th...

Signaling scheme for improved video streaming

Abstract: In one embodiment, a mobile communications system has a plurality of radio access network (RAN) nodes communicating wirelessly with a plurality of wireless user devices, a video server transmitting one or more packetized video streams to one or more of the wireless user devices, and a packet core routing packets of video data from the video server to the one or more wireless user devices via the RAN nodes. For each packetized video stream, the video server transmits in-band information related to the packetized video stream, such as relative screen size, relative video activity level, and minimum and maximum video quality levels, and each RAN node extracts and uses the in-band information to schedule transmission of the packets of video data to its one or more corresponding wireless user devices. The system can provide higher-quality video experience without wasting bandwidth on user devices that don't require much communication resources.

A Buffer Aware Resource Allocation Framework for Video Streaming over LTE

Abstract: Latency sensitive high bandwidth multimedia data is expected to capture more than \(70\%\) of the available bandwidths in LTE and future wireless networks. This is expected to pose enormous challenges on the radio resource and management mechanisms in these networks. One of the most important factors that diminish the quality of viewing experience of delivered videos is frequent client side rebuffering events. This paper proposes a buffer-aware downlink scheduling framework (called BA-TLS) for streaming video applications. The problem of scheduling a set of video streams over LTE has first been formulated as an Integer Linear Programming (ILP) problem. It has been shown that a conventional Dynamic Programming (DP) solution for the ILP imposes prohibitive online overheads. Then, by utilizing analytical properties of the problem, a genetic algorithm based stochastic solution has been implemented. Further, a fast and efficient deterministic heuristic known as Proportionally Balanced Robustness-level Allocator (PBRA) has been implemented over the proposed framework. Experimental results show that while the optimal DP based solution performs significantly better at reducing rebuffering events with respect to the stochastic as well as the deterministic solutions, both of them incur much lower temporal overheads compared to DP. Further, it may be observed that although the stochastic and deterministic solutions are comparable in performance, the deterministic heuristic solution PBRA is about 300 to 600 times faster on average compared to its stochastic counterpart.